{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c11acc27",
   "metadata": {
    "id": "c11acc27",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Build a QSAR model in 8 lines of Python\n",
    "When I encounter a new dataset.  I often want to construct a simple model to get a quick idea of how easy or hard it will be to model the data.  Over the years, I've put together several scripts to do this.  Recently, I've come across a few Python packages that make this whole task a lot easier.  One thing I like about the workflow below is that it's flexible.  I can change the input format, the descriptors, or the machine learning model by changing one line of code.  This script doesn't take the place of rigorous validation but it provides a quick place to start.  \n",
    "\n",
    "Ok, I admit that there are more than 8 lines of code here.  I added some more to illustrate what we're doing.  However, there are only **8 lines** here that are critical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5883a44e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-05T22:12:08.002972Z",
     "start_time": "2025-05-05T22:12:08.001236Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5883a44e",
    "outputId": "32a5ebf4-2348-433e-c636-3507c200dda9",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2mUsing Python 3.12.11 environment at: /usr\u001b[0m\n",
      "\u001b[2K\u001b[2mResolved \u001b[1m100 packages\u001b[0m \u001b[2min 1.78s\u001b[0m\u001b[0m\n",
      "\u001b[2K\u001b[2mPrepared \u001b[1m15 packages\u001b[0m \u001b[2min 2.07s\u001b[0m\u001b[0m\n",
      "\u001b[2mUninstalled \u001b[1m2 packages\u001b[0m \u001b[2min 7ms\u001b[0m\u001b[0m\n",
      "\u001b[2K\u001b[2mInstalled \u001b[1m15 packages\u001b[0m \u001b[2min 140ms\u001b[0m\u001b[0m\n",
      " \u001b[32m+\u001b[39m \u001b[1maiobotocore\u001b[0m\u001b[2m==2.24.2\u001b[0m\n",
      " \u001b[32m+\u001b[39m \u001b[1maioitertools\u001b[0m\u001b[2m==0.12.0\u001b[0m\n",
      " \u001b[32m+\u001b[39m \u001b[1mbotocore\u001b[0m\u001b[2m==1.40.18\u001b[0m\n",
      " \u001b[32m+\u001b[39m \u001b[1mdatamol\u001b[0m\u001b[2m==0.12.5\u001b[0m\n",
      " \u001b[31m-\u001b[39m \u001b[1mfsspec\u001b[0m\u001b[2m==2025.3.0\u001b[0m\n",
      " \u001b[32m+\u001b[39m \u001b[1mfsspec\u001b[0m\u001b[2m==2025.9.0\u001b[0m\n",
      " \u001b[31m-\u001b[39m \u001b[1mgcsfs\u001b[0m\u001b[2m==2025.3.0\u001b[0m\n",
      " \u001b[32m+\u001b[39m \u001b[1mgcsfs\u001b[0m\u001b[2m==2025.9.0\u001b[0m\n",
      " \u001b[32m+\u001b[39m \u001b[1mjmespath\u001b[0m\u001b[2m==1.0.1\u001b[0m\n",
      " \u001b[32m+\u001b[39m \u001b[1mloguru\u001b[0m\u001b[2m==0.7.3\u001b[0m\n",
      " \u001b[32m+\u001b[39m \u001b[1mmolfeat\u001b[0m\u001b[2m==0.11.0\u001b[0m\n",
      " \u001b[32m+\u001b[39m \u001b[1mmordredcommunity\u001b[0m\u001b[2m==2.0.6\u001b[0m\n",
      " \u001b[32m+\u001b[39m \u001b[1mpmapper\u001b[0m\u001b[2m==1.1.3\u001b[0m\n",
      " \u001b[32m+\u001b[39m \u001b[1mrdkit\u001b[0m\u001b[2m==2025.3.6\u001b[0m\n",
      " \u001b[32m+\u001b[39m \u001b[1ms3fs\u001b[0m\u001b[2m==2025.9.0\u001b[0m\n",
      " \u001b[32m+\u001b[39m \u001b[1mselfies\u001b[0m\u001b[2m==2.2.0\u001b[0m\n",
      " \u001b[32m+\u001b[39m \u001b[1mwget\u001b[0m\u001b[2m==3.2\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "IN_COLAB = 'google.colab' in sys.modules\n",
    "if IN_COLAB:\n",
    "    !uv pip install --system rdkit pandas datamol molfeat numpy scikit-learn yellowbrick wget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ad300921",
   "metadata": {
    "id": "ad300921",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import datamol as dm\n",
    "from molfeat.calc import FPCalculator\n",
    "from molfeat.trans import MoleculeTransformer\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import HistGradientBoostingRegressor\n",
    "from yellowbrick.regressor import prediction_error, residuals_plot\n",
    "import wget"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bDXYM8qG8qDU",
   "metadata": {
    "id": "bDXYM8qG8qDU"
   },
   "source": [
    "# **0.** Get an input file.  \n",
    "To start we need a csv file with Chemical structures as SMILES and some property or actvity file.  The file should have a column named **SMILES** and an activity column whose name we will specify below.  If you're running in Google Colab, you'll need to upload the file from your local machine you can do this by first clicking on the **Files** icon on the left, then clicking on the \"Upload to session storage\" icon in the menu that opens up.  After that, you should see the file in the file explorer on the left.\n",
    "\n",
    "![QSAR_in_8_lines_ipynb_-_Colab.png]()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rBoN3jj__fI7",
   "metadata": {
    "id": "rBoN3jj__fI7"
   },
   "source": [
    "The code below will download a demo file called \"carbonic.csv\" from GitHub.  If you don't have your own csv file, you can use this one to try out the code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "KYjR4cCh_8zC",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "KYjR4cCh_8zC",
    "outputId": "447c3cf3-b4dc-4de5-c49e-3753925823f0"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'carbonic.csv'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wget.download(\"https://raw.githubusercontent.com/PatWalters/datafiles/refs/heads/main/carbonic.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41037477",
   "metadata": {
    "id": "41037477",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## **1.** Read the data into a [Pandas](https://pandas.pydata.org/) dataframe.    \n",
    "In the next cell we read the data from a csv file and put it into a Pandas dataframe, a structure that holds a datatable in memory for a Python script to use. If you want to use your own csv file, upload it as described above, then change\n",
    "```python\n",
    "filename = \"carbonic.csv\"\n",
    "```\n",
    "to\n",
    "```python\n",
    "filename = \"myfile.csv\"\n",
    "```\n",
    "where `myfile.csv` is the name of the file you uploaded.  The code below uses the last column in the dataframe as the the acivity column.  If this isn't the case, change the code below to assign the correct column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e33f7474",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 224
    },
    "id": "e33f7474",
    "outputId": "f78c5e2c-c0c6-4e08-cd43-6a79300f9519",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The activity column is proably pIC50\n"
     ]
    },
    {
     "data": {
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       "summary": "{\n  \"name\": \"df\",\n  \"rows\": 556,\n  \"fields\": [\n    {\n      \"column\": \"SMILES\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 544,\n        \"samples\": [\n          \"CC1=NN(C(=O)C1=CC=Cc2ccccc2)c3ccc(cc3)S(=O)(=O)N\",\n          \"COCCN(CCOC)Cc1cc2cc(oc2s1)S(=O)(=O)N\",\n          \"c1cc2c(cc1Br)c(cs2)CNS(=O)(=O)N\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 556,\n        \"samples\": [\n          \"CHEMBL2336905\",\n          \"CHEMBL155420\",\n          \"CHEMBL543798\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pIC50\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.319600721019794,\n        \"min\": 4.0,\n        \"max\": 9.3,\n        \"num_unique_values\": 273,\n        \"samples\": [\n          8.55,\n          6.23,\n          6.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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       "          quickchartButtonEl.style.display =\n",
       "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "        })();\n",
       "      </script>\n",
       "    </div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "                                              SMILES          Name  pIC50\n",
       "0     CC(C)CN(C)Cc1cc(ccc1O)C(=O)c2cc(sc2)S(=O)(=O)N  CHEMBL544127   8.05\n",
       "1               COc1ccc(cc1)C(=O)c2cc(oc2)S(=O)(=O)N  CHEMBL340519   8.70\n",
       "2          CN(C)Cc1cc(ccc1O)C(=O)c2cc(sc2)S(=O)(=O)N  CHEMBL541089   8.19\n",
       "3    c1cc(ccc1CN2CCOCC2)S(=O)(=O)c3cc(sc3)S(=O)(=O)N  CHEMBL555153   8.12\n",
       "4  CC(C)CN(C)Cc1cc(ccc1O)S(=O)(=O)c2cc(sc2)S(=O)(...  CHEMBL539817   8.30"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = \"carbonic.csv\"\n",
    "df = pd.read_csv(filename)\n",
    "activity_col = df.columns[-1]\n",
    "print(f\"The activity column is proably {activity_col}\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8488ccfb",
   "metadata": {
    "id": "8488ccfb",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## **2.** Instantiate a Fingerprint calculator from the awesome [molfeat](https://m2d2.io/blog/posts/introducing-molfeat-a-hub-of-molecular-featurizers/) package. This package has several descriptor types available.\n",
    "```python\n",
    ">>> from molfeat.calc import FP_FUNCS\n",
    ">>> FP_FUNCS.keys()\n",
    "dict_keys(['maccs', 'avalon', 'ecfp', 'fcfp', 'topological', 'atompair', 'rdkit', 'pattern', 'layered', 'map4', 'secfp', 'erg', 'estate', 'avalon-count', 'rdkit-count', 'ecfp-count', 'fcfp-count', 'topological-count', 'atompair-count'])\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6ff42711",
   "metadata": {
    "id": "6ff42711",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "calc = FPCalculator(\"ecfp\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98a1b30f",
   "metadata": {
    "id": "98a1b30f",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## **3.** Instantiate a molecule transfomer from molfeat.    \n",
    "This object takes a list of SMILES as input and returns descriptors.  It's very flexible and can run in parallel.  [Check it out!](https://molfeat-docs.datamol.io/stable/tutorials/types_of_featurizers.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4e9bd7ae",
   "metadata": {
    "id": "4e9bd7ae",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "trans = MoleculeTransformer(calc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bda1548",
   "metadata": {
    "id": "6bda1548",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## **4-5.** Calculate the fingerprints.    \n",
    "Note the use of the function from [datamol](https://datamol.io) that silences logging messages from the RDKit.  This is more polite version of my rd_shut_the_hell_up function in [useful_rdkit_utils](https://github.com/PatWalters/useful_rdkit_utils)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f8cedeaa",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f8cedeaa",
    "outputId": "0f1ec1ac-6076-4674-87f8-97a096dea2c7",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 171 ms, sys: 1.11 ms, total: 172 ms\n",
      "Wall time: 173 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "with dm.without_rdkit_log():\n",
    "    df['fp'] = trans.transform(df.SMILES.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec6f5322",
   "metadata": {
    "id": "ec6f5322",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## **6.** Split the data into training and test sets.  \n",
    "I like to do this with dataframes.  That way I don't have to remember the order in which `train_X, test_X, train_y, and test_y` are returned by [train_test_split](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "37a488a6",
   "metadata": {
    "id": "37a488a6",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "train, test = train_test_split(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0ff42cd",
   "metadata": {
    "id": "d0ff42cd",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## **7.** Instantiate an sklearn style regressor.  \n",
    "In this case I used [HistGradientBoostingRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html), which is the scikit-learn implementation of [LightGBM](https://lightgbm.readthedocs.io/en/latest/Python-Intro.html).  You can easily plug in any scikit-learn compatible regressor like [RandomForest](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html) or [XGBoost](https://xgboost.readthedocs.io/en/stable/python/python_intro.html).\n",
    "```python\n",
    "from lightgbm import LGBMRegressor\n",
    "model = LGBMRegressor()\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "model = RandomForestRegressor()\n",
    "from xgboost import XGBRegressor\n",
    "model = XGBRegressor()\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "21d70cae",
   "metadata": {
    "id": "21d70cae",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "model = HistGradientBoostingRegressor()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "042568ca",
   "metadata": {
    "id": "042568ca",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## **8.** Use [YellowBrick](https://www.scikit-yb.org/en/latest/) to build a model and visualize its performance.\n",
    "The **Loss** reported in the plot below is the [$R^2$](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html) for the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "04b4caa7",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 561
    },
    "id": "04b4caa7",
    "outputId": "114b7e9b-866b-4634-a4fa-18785a610183",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5.4 s, sys: 1.47 s, total: 6.87 s\n",
      "Wall time: 6.93 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "visualizer = prediction_error(model,np.stack(train.fp),train[activity_col],np.stack(test.fp),test[activity_col])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec329324",
   "metadata": {
    "id": "ec329324",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Bonus\n",
    "Plot the residuals for the training and test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e5853f4b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 524
    },
    "id": "e5853f4b",
    "outputId": "450e8de0-06b0-4bf9-daa5-042929c1d20e",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dOk488UTKysqYOnUqVuv+r5xDDz004nnd9nxYvXo1s2fPJiMjI/RaSkoK8+bNY+3atQAcfvjh/OMf/+C8887jhz/8IQcffDAjRowIHf+u9nG0x7OjNnZmypQpoSomhx56KJs2bWLt2rX87ne/a7dscXExKSkp7caJzJ07l+eee46tW7eilMLn8zF9+vR2y7ROW4pmvwkh+pYE3QJoH2isWbOG//f//h8XXHBBWLqBy+UC4IYbbmj3OB8C+dQAd955Jy+++CJvvvkmzz//PHa7neOPP57rrrsu7EsgqLGxkdTU1LDX0tLSur0djY2NnH322dhsNq655homTZqEzWbj7rvv7jCXMTU1lRdffJHHH3+c119/nT//+c8MGTKE8847j4suuigUjHbF5XJF3LZgoN3Y2Bj6d2ZmZtTbVFBQEDagLcjhcHRYTnD48OG89NJLPP744zz11FPccccdjBo1il/84hecfvrpEf9m1KhRAJSVlTFt2rTQ64ceemhYUHbOOee0G4DYdrufeuopli5dyuLFi7n++uvJysqivLw8LHe6sbERoFvHPXj+/exnP0PX92fHBQP64PkX6X2Dx1G1yRlvy2KxMHbs2NDPY8eOZc6cOfh8vlBaldvtBvYf29bS09ND2xbNOdGTY9WRSNvc9man7f5tfWPSWuubvT179nDBBRfwox/9iJNPPjm0TPB4/OUvf2lXYcbtdlNRURFa94gRI8J+b7fbIx7rtvvL5XKxYcOGdjchXq831PaFCxfyzDPP8Mwzz3DbbbfR0NDA7NmzWbJkCd/5zne63MfB7ejqeHbUxq6cdNJJ3HfffdTX1/Paa68xZswY5syZ065yjsvlIi0trd1nTuvzJXg82+67tj9Hs9+EEH1Lgm4BRA40/vWvf3HbbbdxyCGHhHqJgwPorrnmGg477LB27xP8MrLZbJxzzjmcc8451NbW8v777/OHP/wBv9/PXXfd1e7vnE5nu4GOwUFUQR0FTY2NjaEetC+//JKKigoee+wxvve974WWaWpq6nT7c3Nzueaaa7jmmmsoKyvj5Zdf5k9/+hO5ubmcdtppnf5tUGZmZig/NNJ2RPpCj6fRo0dz0003cdNNN7Fp0yaeffZZbrjhBkaPHt0ulxQCg76ys7N57733Qj2ZEPgyb/2F3rq3siPLli1jzpw5/P73vw+91jZ/Nxggut1unE5n6PW2x7214Pl39913U1RU1O738QwmZs6cSXNzM9u3b2f8+PHA/qCzNZfLFbqBifac6O6x6gm73Q6039/BYLyt1jd7Y8eO5dxzz+WBBx7gqKOOCpX6DF7v5513XsQbhOAxttvt7a5vn8/XLpiNJDMzk+HDh/N///d/7X7X+sbrgAMO4IADDsDv9/PNN9/wwAMPcNFFF/Hhhx+SmZnZ6T4O9ix3dTx76sQTT+See+7hvffe46233upwAGZmZmYosG4deAfPl8zMzFCefPDGL6jtE5xo95sQou/IlSc6dP311+N2u1m6dGnotXHjxpGZmUlZWRljx44N/Td69Gj8fj+5ubnU1tbyxhtvhCqVZGdnc/rpp3PSSSdRUlIScV0TJkxg1apVYa99/fXXYT8He4drampCr9XX17Nt27bQz8Ee2NbB186dO/nyyy877OEsLS0Nm/CjoKCAK6+8kkmTJrF+/fqOd1Abs2bNoqysrF3Vhm+++YYJEyb0qOe+p0pKSsImc5k0aRK33HIL6enpYdvUep/YbDbOP/983nnnnQ4ngikvL+80KA7y+XyhdI6g1157LWydwcC1dQ14pVSnkzHNmDEDi8XC7t27w86/YcOGoet6t3sgW7enK1u3bgUCpfPS09OZOHEiX331VdgyFRUVlJWVhYK4aM6JnhyrngjeVBcXF4dec7vdUU+Mcvnll5OTk8ONN94Y1ttaVFTEtm3bwo7H2LFj8Xq9DBkyJLTutWvXhj4TIFAHPFLJxrbmzJnDtm3bGDFiRNj7K6VCaTuffPIJmzdvBgI3hQcddBDXXXcdjY2NlJWVdbmPoz2ePZWfn89BBx3Ek08+SXl5OSeeeGLE5WbNmkVzc3O7eRG++eYb0tPTKSwsZOzYsVit1i4/L6PZb0KIviVBt+jQ8OHDueKKK3j99df5z3/+AwS+0C688EJeeOEFnnnmGUpLSykpKeG6667j9NNPp7y8HKUUv//977nhhhtYv349e/bs4bPPPmPFihV897vfjbiuH/7wh6xZs4ZHHnmE7du3s2LFCp566qmwZcaMGUNWVhbPP/88GzdupKSkhGuuuYahQ4eGlpkxYwZWq5UnnniCsrIyPv/8cy677DKOPfZYamtrWbduXVhpOQjkul5++eU8+eSTlJaWsmvXLl599VW2bdvGgQceGPX+OvXUU8nOzubKK69k9erVbNu2jfvuu4+PP/6Yn//851G/TyysXLmSSy+9lFdeeYWysjLKysp44oknaGpq4jvf+Q4QuImprKzk66+/DpVWu/DCC/nBD37AxRdfzJ/+9CfWrVvH7t27Wb16NX/5y1846aSTyMjICOsJj2TOnDl8+eWXfPbZZ2zfvp0//OEPmKaJxWJh9erVVFdXc/DBB5Ofn88999zDypUr2bx5MzfddFOnTyWGDh3KaaedxgMPPMDrr79OWVkZq1at4pe//CVnn312u96/zgR7zT/66KOwso2maVJZWRn6r7S0lL///e88+OCDnHXWWQwfPhyAiy66iE8++YQHHniA0tJSVq5cya9+9Suys7NDPZnRnBM9PVbdNX/+fLKzs0P7e8OGDVx11VXtbo46kpaWxvXXX89///tfXnrppdDrF198Mf/+97+5//772bJlC5s3b+bOO+/klFNOCeVCH3vssVRWVnLXXXexbds2PvjgAx599NGobpLOPfdcGhsbueqqqyguLqasrIx//OMfnHzyyaGUuFdffZXLLruMTz/9lN27d7Nx40aefPJJhgwZwoQJE6Lax9Ecz9744Q9/yObNm5k+fXrohrOtI488kgkTJoT2844dO3j22Wd5+eWXOf/887HZbKSnp3PkkUfyj3/8g/fee4/t27fz3HPPtSt/GM1+E0L0LUkvEZ0699xzee211/jd737HW2+9hdPp5OKLLyYtLY3nn3+eu+66C7vdzoEHHsjzzz8fGsT25JNPcu+993LOOefg8XgYPnw4xxxzDL/61a8irucnP/kJ5eXlPPnkk9x///1Mnz6dW2+9NeyRdWpqKn/4wx+44447+NGPfsSIESO4/PLL+eCDD9i1axcQyEu+7bbbuO+++zjhhBMoKirid7/7HTk5OXz11VecddZZYQEDwGGHHcbtt9/OU089xb333oumaYwdO5Ybbrihy+CytdzcXJ599lnuuusuzj//fJqbmxk/fjx33nlnWB5sX1i8eDFut5vHHnuMW265BZvNxsSJE7n33nuZNWtWaJlPP/2U8847j8WLF/Pb3/4Wq9XKvffeyz//+U9eeuklXnzxRRobG8nKyqKoqIhf/vKXnHbaaRFzzFv79a9/TWVlJZdffjkpKSmcdNJJ3HTTTaSmpvLCCy+gaRpLly7loYce4uabb+bss88mKyuL008/ncWLF/OnP/2pw/f+3e9+R15eHvfffz979+4lLS2NQw89lOeeey4sbaIrCxcuZN68edxxxx0UFRWFailXV1dz6KGHhpZLS0tjzJgxXH311WEzj5588smYpsmTTz7JQw89hMPh4Lvf/S633XZb6ElLNOdET47VT3/606i3s/V2/PWvf+XWW2/l7LPPZvjw4Vx44YVs376d7du3R/UeRx99NIcddhh/+MMf+P73v09eXh4nnHACuq6Han5brVZmzpzJY489FhpweMIJJ7Bjxw6ef/55XnzxRWbNmsXSpUs555xzQmkvHRk7dizPPvssf/rTnzj33HPx+XwUFhayZMmS0PG49dZbufvuu/ntb39LVVUVmZmZzJ49myeeeAKHwxHVPo7mePbGUUcdxc0339xu0GlrdrudJ598kjvvvJMrrriCxsZGRo0axdVXXx12zG+++WZuuukmlixZgqZpLFy4kBtvvDHs5j6a/SaE6Fua6u0zSyGEEP1CpAGDl156Kdu3b+90MqjeUkpRWVnJsGHDQrnKdXV1fPe73+Waa67hwgsvjNu6hRAiWUh6iRBCDAJ+v5+TTjqJ8847j1WrVlFWVsbf/vY3Pvjgg25XSemuzz//nO9973v88Y9/ZMeOHaxfv57rrruO1NTUdqVChRBioJKebiGEGCRKS0v5wx/+wDfffIPb7Wb06NGcfvrpnHPOOR3ONhory5YtC42bsNvtTJ06lSuvvLLb9fCFEKK/kqBbCCGEEEKIOJP0EiGEEEIIIeJMgm4hhBBCCCHiTIJuIYQQQggh4kzqdLf49ttvUUphs9kS3RQhhBBCxJnP50PTNObOndvpchIfJJdoj1sykp7uFkqpsGmWlVJ4vd5eT70sek+ORfKQY5E85FgkDzkWyaM7x6Lt935vlxN9oz8fD+npbhG8g505cyYATU1NlJSUMHHiRFJTUxPZtEFPjkXykGORPORYJA85FsmjO8eiuLg4qvdsGx+IxIr2uCUj6ekWQgghhBAiziToFkIIIYQQIs4k6BZCCCGEECLOkjro3rVrF5dddhkHHXQQCxYs4Nprr6W+vj7isu+88w4nnngic+fO5dRTT+XTTz/t49YKIYQQQohkkkyxZFIPpLzkkkuYMWMGK1asoKGhgcsuu4w777yT2267LWy5kpISlixZwgMPPMDBBx/Mv/71Ly6//HLeffddhg8fHrP2+Hw+/H5/zN5PRKe5uTn0f03TEtyavmOxWLDZbINqm4UQQohYSqZYMml7uuvr65kxYwZXXXUVaWlpDB8+nFNOOYWvv/663bIvvfQSCxcuZOHChaSkpHDSSSdRVFTEsmXLYtaehoYGmpqaYvZ+Inp2u51x48Zht9sT3ZQ+5fV62bdvn9zoCSGEED2QbLFk0vZ0Z2ZmsnTp0rDX9uzZQ15eXrtl165dy8KFC8NemzZtWszKypimic/nIzc3NybvJ7rHMAwAHA4HFoslwa3pW2lpaezbt4+hQ4dKj7cQQgjRDckUS0ISB91tFRcX89xzz/HXv/613e9qa2vJysoKey0rK4vNmzd3ax1KqVBvttvtDv3f6/Vis9lCwZ/oW8Ei+EqpQXkMUlJSqK+vT4rZ0FpfFyKx5FgkDzkWyaM7x0IpFXVnRuv4QCRWd45bW30RS3amXwTd33zzDb/4xS+46qqrWLBgQcRlYjE7kc/no6SkJOy10tJSAMaNG4fH4+n1OkTPBXO7Bxu/38+2bdsS3YwwwetCJJ4ci+QhxyJ5RHssok1bjBQfiMTpSbppX8WSnUn6oHvFihVcc8013HjjjZx88skRl8nJyaG2tjbstdra2m6ng9hsNiZOnAgE7pJLS0spLCxE13XsdjsOh6MnmyB6SSlFc3MzKSkpgzbFYvz48aSkpCS6GWHXhdPpTHRzBjU5FslDjkXy6M6x6E4PZuv4QCRWT3qe+zKW7ExSB93/+9//WLJkCffeey+HHnpoh8vNmDGDNWvWhL1WXFzM8ccf3631aZrWbtpYp9MZCvQGWz5xsgimlGiaNiiPga7rOByOpPoydzqdMt11kpBjkTzkWCSPaI5FdzpxIsUHIjG62/nW17FkZ5K2eonf7+eGG27g6quvjriTfvrTn/LOO+8AcMYZZ/DZZ5/x4Ycf0tzczMsvv0xpaSknnXRSXzdbCCGEEEIkgWSLJZO2p3vlypVs2bKF//u//+P//u//wn737rvvUlZWRl1dHQBFRUXcfffdLF26lF27djFx4kQefvhhhg0bloimi17as2cPv/nNb6iqqsJisXDJJZfw/e9/P9HNEmJAcHsb2Fq5Cp/hwWZxMH7YbJz2jEQ3SwghYi7ZYsmkDboPOOAANmzY0OHvV6xYEfbzUUcdxVFHHRXvZok+YLFYuP7665k6dSqVlZWceuqpHHTQQZJTL0QvmMpkddkKyuu2YSoTTdNQSrGzuoT8rHHMKjgCXUvah59CCNFtyRZLyiesiNquXbuYOXNm3Ctp5OXlMXXqVACGDRtGTk5O6E5UCNEzq8tWsLtmE4r95bY0TUOh2F2zidVlK7p4ByGEEL2RtD3dovd+9rOf8dVXXwGBwYimaYbVen733XcZNWpU1O83atSoXheJP/vss0NtstlsFBQUcOmll3LiiSdGXH7NmjUYhhGzKViDnn/+eR5//HEqKyuZMmUKN954I7NmzepweZfLxb333svy5cupqqpi2rRpXH/99WF/89VXX/H444+zZs0aKisr+ctf/sKiRYvC3ueII45g165d7d7/Jz/5CTfddFPsNlCIVtzeBsrrtqHrkQci67qF8rptuL0NMUs1kTQWIYQIJ0F3D3n9Bqt21/TpOmePzMFujb56xxNPPBH69/33388nn3zCP/7xj3g0LSpKKdatW8eSJUs48cQT8Xg8PPvssyxZsoQ5c+ZQUFAQtnxtbS1Llizh5ptvjmk73nnnHZYuXcrNN9/M7Nmzefrpp7ngggt49913GTJkSMS/ueGGG9i0aRN33XUXeXl5LFu2jPPPP5933nmH/Px8AJqampg8eTI/+tGPuPzyyyO+z8svvxw2wc+mTZs4//zzOeaYY2K6jUK0trVyVSilpCOmMtlauYrpozoe3R8NSWMRQojIJOjuoVW7azj43n/26Tq/+NWxHDhmaEzfc/LkyVx33XU89thjnHvuufz85z+nuLiYpUuXsnHjRux2Oz/4wQ+44YYbKC8v58gjj+Sdd95hwoQJTJ48mfvvv58nn3ySkpISCgoKuPPOO5k2bVrEdZWWltLY2Mj3vve90MCE0047jaeffppt27aFBd1er5fLLruMiy66iLlz58Z0YqInn3ySM844gx/96EcA3HzzzXz44Ye88sor/PznP2+3vMfj4b333uPBBx/kwAMPBOCKK67ggw8+4G9/+xtXXnklAAsXLmw3hWxbbet9PvLII4wZM4bvfve7sdg0ISLyGZ4uy2xpmobP6P11Fkxj0XVLxDQWgDljFnX2FkIkHZenmqc+vTb083mH3pHA1oj+SoJuwfLly3n99ddDvbxXXnklJ510Es8++yzl5eWceeaZTJw4kcMPP7zd3z722GPccccdjBgxgssvv5w//elPPProoxHXs3btWrKyskITDOzdu5c//elP2O12Jk+eHFpOKcW1117LwQcfzMknn9zh1O8PPfQQDz/8cKfb9vbbbzNy5MjQz16vl7Vr13LxxReHXtN1nQULFvDtt99GfA+/349hGO0mp0lJSeF///tfp+vvjNfrDfWYD9ZJf0TfsFkcXU6drJTCZundYOVEpLEIIUR/IUG34Nhjj2Xo0P096K+//jp2ux2LxcLIkSM58MADWbNmTcSg+4c//CHjx48HAvnKjz/+eIfrWbt2LQ0NDcybNw/DMGhubsbhcHDzzTeHUjQgMFXrO++8w+TJk1m+fDlKKW655RZmzpwZ9n5nnnkmxx57bKfblpeXF/ZzTU0NhmG0SyMZMmQIW7dujfge6enpzJ07lwcffJDx48czdOhQ3nrrLVauXMmYMWM6XX9nli9fTkNDA6ecckqP30OIaIwfNpud1SUoOp7iWNd0xg+b3av19GUaixBC9DcSdIuwnmCAL774gr/85S+Ulpbi9/vx+/0d5hyPHj069G+n00lzc3OH61m3bh1nnXUW55xzDvX19dx1113MmzePU089NWy5Aw44gPXr14d+NgwjYnpJdnY22dnZ0Wxir911111cf/31HHbYYVgsFqZNm8bxxx/P2rVre/yer7zyCocddljYDYcQ8eC0Z5CfNS6U9tGWaRqMzJnU697nvkxjEUKI/kaCbhE2tfqWLVv41a9+xZIlSzjjjDNwOBxcc801+P3+iH/bnbSIdevWccYZZzB27FgAbrrpJk466SROP/30sOA9Wj1JL8nJycFisVBVVRW2XFVVVVhvf1tjxozhueeeo6mpCZfLRV5eHr/+9a/bDf6M1q5du/jss8+4//77e/T3QnTXrIIjANoNcNQ1nZE5k0K/742+SmMRQoj+SIJuEaakpAS73c65554LBL4gS0pKmDRpUq/et6ysjPr6+rD3mThxIgUFBbz11ltccskl3X7PnqSX2O12pk+fzueffx4q52eaJp9//jlnn312l+tMTU0lNTWVuro6Pv30U6655pputxvg1VdfZciQITLTpugzuqYzZ8yisFJ+dquTcUNn47Snx2QdfZXGIoQQ/ZEE3SLMqFGj8Hg8lJSUMHLkSB5++GHsdjsVFRUo1fEXaVfWrFmDzWajsLAw7PX58+fz/vvv9yjo7ml6yfnnn8+SJUuYMWMGs2bN4umnn8btdoeluTz33HO8//77PP300wB88sknKKUYN24cO3bs4K677mL8+PFhf9PY2MiOHTtCP+/cuZOSkhKysrLCettN0+TVV1/l5JNPxmqVS1D0Lac9I2751H2VxiKEEP2RfOOLMHPnzuWss87i7LPPxul08otf/ILrr7+eX/ziF9xzzz09ft9169YxduxY7HZ72OsLFizgxRdfZO/evTGfAKcjxx13HNXV1dx3331UVlYydepUHnvssbD0kpqaGsrKykI/NzQ08Mc//pG9e/eSnZ3NUUcdxZVXXhk22dCaNWtCTwgAli5dCsApp5zCHXfsLy/12WefsXv37lDJQiEGkr5IYxFCiP5IU73pvhxAgjMtBitkNDU1UVJSwtSpU0P5iU6nM7T8Vzv2DYg63f1BcCClw+EIyz8fLNxuNxB+/iVK6+siNTU10c0Z1JL9WMQzjSXZJPuxGEy6cyzafu/3djnRN/rz8ZCe7h6aPTKHL37VeT5xPNYphBD9QTzTWIQQoj+SoLuH7FbLoOx1FkIIIYQQ3acnugFCCCGEEEIMdBJ0CyGEEEIIEWcSdAshhBBCCBFnEnQLIYQQQggRZxJ0CyGEEEIIEWcSdAshhBBCCBFnEnQLIYQQQggRZxJ0CyGEEEIIEWcSdAshhBBCCBFnEnQLIYQQQggRZxJ0i4Tas2cP55xzDscddxwnnngi//znPxPdJCGEEEKImLMmugFicLNYLFx//fVMnTqVyspKTj31VBYuXEhqamqimyaEEEIIETMSdIuEysvLIy8vD4Bhw4aRk5NDXV2dBN1CCCGEGFAk6O4hw/RT07inT9eZkzYCix79IfvZz37GV199BYBhGJimic1mC/3+3XffZdSoUT1uz3vvvcfkyZMZO3ZsxN+fffbZofXbbDYKCgq49NJLOfHEEyMuv2bNGkzTZMSIET1uU0eef/55Hn/8cSorK5kyZQo33ngjs2bNirisYRjcf//9LFu2jH379pGXl8cpp5zCpZdeiqZp3XrP7qxXCCGEEAOXBN09VNO4h7dW/aVP13nC7MsYmlEQ9fJPPPFE6N/3338/n3zyCf/4xz9i1p777ruP3/zmNxGDbqUU69atY8mSJZx44ol4PB6effZZlixZwpw5cygoCN+O2tpalixZwq233hqz9gW98847LF26lJtvvpnZs2fz9NNPc8EFF/Duu+8yZMiQdss/+uijvPDCC9x5551MnDiRNWvWcN1115GRkcG5554b9Xt2d71CCCGEGLhkIOUgt2vXLi655BIOOuggDjzwQH7zm9/gcrlCv3/kkUc4/PDDmT17NkcffTRvvPEGACeddBKbNm3i0ksv5brrrmv3vqWlpTQ2NvK9732PYcOGUVBQwGmnnYZhGGzbti1sWa/Xy2WXXcZFF13EvHnzYr6NTz75JGeccQY/+tGPmDhxIjfffDMOh4NXXnkl4vLffvstRx55JN///vcZPXo0xxxzDIceeiirV6/u1nt2d71CCCGEGLgk6B7ElFJceumljBgxgg8//JB3332X8vJy7rzzTgD+97//8cwzz/D888+zcuVKbrzxRn7/+99TVVXFsmXLAHjwwQdZunRpu/deu3YtWVlZTJw4EYC9e/fypz/9CbvdzuTJk8PacO2113LwwQdz8sknd9jWxx9/nO985zvMnTu3w/92797d7u+8Xi9r165lwYIFodd0XWfBggV8++23Edc1d+5cvvjii9DNwfr16/nmm2847LDDon7PnqxXCCGEEAOXpJcMYsXFxWzatIkXXngBp9OJ0+nkiiuu4IILLuCWW26hoaEBXddxOBxomsahhx7KN998g653fa+2du1aGhoamDdvHoZh0NzcjMPh4OabbyY/Pz+03DfffMM777zD5MmTWb58OQB33XVXWGAOcNppp3HCCSdgsVg6XGdwQGZrNTU1GIbRLp1jyJAhbN26NeL7/PznP8flcnHsscdisVgwDIMrr7ySk046Ker37Ml6e6Oiwc0L35ZS5/GS5bCzeG4heRnOmK9HCCGEED0jQfcgVlZWhmEYHHTQQWGvG4ZBTU0N8+fPZ9q0aRxxxBHMnz+fww47jB/+8IdRVRZZt24dZ511Fueccw719fXcddddzJs3j1NPPTVsuQMOOID169d3+X5ZWVnk5+d3GnTHyj//+U/efPNN7rnnHiZOnEhJSQlLly4NDahMJoZpcvvyYj7aUo5hKnRNw1SKN9eWsXBCPtcvmoklipskIYQQQsSXBN2DWEpKCqmpqZ2mOzz00EOsX7+ef//73zz//PM88cQTvPrqq2RkZHT63uvWreOMM84IDbK86aabOOmkkzj99NMZPXp0t9v6+OOP88QTT4RVD2nr7bffZuTIkWGv5eTkYLFYqKqqCnu9qqqKoUOHRnyfu+66i5///Occf/zxAEyePJndu3fz8MMPc8opp0T1nj1Zb0/cvryY5Rv3YNV19JZ9o2saSsHyjYHqOjceNTtm6xNCCCFEz0gX2CA2ZswYmpqaKCsrC73mcrmoqakBwOfz4XK5mDJlCpdddhmvv/46mqbx2Wefdfq+ZWVl1NfXM2nSpNBrEydOpKCggLfeeqtHbT3ttNN49dVXef311zv8L1J6id1uZ/r06Xz++eeh10zT5PPPP2fu3LkR1+XxeNoF9xaLBaVU1O/Zk/V2V0WDm4+2lGPtoCfbqut8vKWCigZ3TNYnhBBCiJ6ToHsQKyoqYu7cudx2221UV1dTX1/PTTfdxG9+8xsgUHLwoosuYu/evQBs2bKFuro6xowZAwR6yrdv3x5W7QQC9bZtNhuFhYVhr8+fP5/333+/R23Nyspi7Nixnf5ntUZ+cHP++efzj3/8g9dee40tW7bw+9//HrfbHUp1ee655/jpT38aWv7www/noYce4sMPP2Tnzp28//77PPnkkyxatCjq94x2md544dtSDFN1uozfNHnh29KYrE8IIYQQPSfpJYPcPffcwy233MKRRx6J3W5n/vz53HHHHUAgaNy9ezcnn3wyHo+HESNGcPXVVzN16lQAzjzzTO666y4+++wz/vrXv4bec926dYwdOxa73R62rgULFvDiiy+yd+9ehg8f3mfbeNxxx1FdXc19991HZWUlU6dO5bHHHguledTU1IT19t9www3ce++93HzzzVRVVZGXl8ePf/xjLrvssqjfM9pleqPO4w2llHRE1zTqPb6YrE8IIQarbdUu5lz1LMY95yS6KaIf01TwmfkgV1xcDMDMmTMBaGpqoqSkhKlTp4ZSDZzO/dUg9jWUJf3kOAOFYRh4PB4cDkefDKRMNm53ID2k9fkHcO/HJbxevKPTwNtUipNnjuFXh02NSVtaXxfRDKgV8SPHInnIsUge3TkWbb/3O1tuW7WLU5ZtlqA7CUR73JKR9HT3UE7aCE6YfVnXC8Z4nUIELZ5byJtry+jsttmq6yyeW9hnbRJCCCFEZBJ095BFtw7KXmeRPPIynCyckB+qXtKW3zRZVDRC6nULIYQQSUCCbiH6sesXBR6vfbylAr9phup0W3WdRUUjQr8XQgghRGJJ0C1EP2bRdW48anZoRsp6j48sh43F8woZli493EL0R25vA1srV+EzPNgsDsYPm43T3vncCEKI5CdBtxADQF6GM2aDJYUQiWEqk9VlKyiv24apTDRNQynFzuoS8rPGMavgCHRNKv0mwrBUL4+fsjbRzRD9nFy9QgghRBJYXbaC3TWbUKhQ1SxN01AodtdsYnXZigS3UAjRG0kfdH/yyScsWLCAK6+8stPlrr32WqZNm8bMmTND/x1wwAExaYPFYsHv98fkvYToLq/X2+HEP0KIgcHtbaC8bhu6Hrksqq5bKK/bhtvb0MctE6J/S4Y4Miipv8kfffRRXn75ZcaOHRvV8r/4xS+44oorYt4Om82Gy+WioaFBgp8EME2T5uZmAPQOpjwfiILbbbPZsNlsiW6OECKOtlauCqWUdMRUJlsrVzF91KF92DIh+q9kiSODkjqCSUlJ6dbOihdN08jJySElJSWh7RisvF4v27Ztw+v1JropfcpqtZKdnU1GhgygEmKg8xmeTgNuCHwX+QxPH7VIiP4vWeLIoKTutj333HO7tfwXX3zBv//9b7Zv386ECRP4/e9/z4wZM6L+e6UUTU1NwP5ZAIP/F4ljmmbo/4NpAlXDMDAMI9HNCCPXRfKQY5E8YnIsjEAaY2eBt1IKDEvoe0q0151joZTq8kanLdn3ided49bXcWRXkjro7o6CggJ0XedXv/oVaWlpPPDAA/zsZz/jX//6Fzk5OVG9h8/no6SkJOy10tLSOLRW9IQci+QhxyJ5yLFIHr05Fj7TjtvnATrrWNDw+O2UVJd0soyA6I+F3W7v1vu2jRFEYnT3uEUjFnFkVwZM0H3ZZeFTsl9zzTW89dZbLF++nNNPPz2q97DZbEycOBEI3CWXlpZSWFiI0yn1jhNJjkXykGORPORYJI9YHQvLnnrK67eiRSgLqJRJfuZ4po+Y25umDnjdORabN2/u9vtPnSqlWROtJ8ctGrGII7syYILutiwWCyNGjKCioiLqv9E0jdTU1LDXnE5nu9dEYsixSB5yLJKHHIvk0dtj8Z3xR0es061rutTp7qZojkV3U0sAudaSQE+OW0/0JI7syoAIupVS3HHHHZxyyilMmTIFCAy+27FjBwUFBQlunRBCCNE1XdOZM2ZR2IyUdquTcUNn47SnJ7p5QgxYfRVH9ttb5vLyco455hjKysrQNI2dO3dy8803U15eTmNjI3fffTc2m41FixYluqlCCNFrbm8Da3d9ysody1m761M8XleimyTixGnPYPqoQ5kzZhHTRh4iAbcQcZCIODKpe7pnzpwJEJqYZvny5QAUFxfj8/nCysjddttt3HnnnZx66qm4XC5mzZrF008/LY+ChBD9WkdTg2+vXAO+VCaryYluohBCJKVkiyOTOuguLi7u8HejR49mw4YNoZ+zs7NZunRpXzRLCCH6THBqcF23hE0NbmLSaJRTsvcTDpxwbIJbKYQQySfZ4sikDrqFECIZtc65tVkcjB82G6c99pMYdTU1uKbpVDbswO1tiMv6hRBCxI4E3UIIEaWOUj12VpfEpbpEdFODGzI1uBBC9AP9diClEEL0tWCqh0KFpXooFLtrNrG6bEVM1ydTgwshxMAhQbcQQkShq1QPXbdQXrcNt7chZuu0WRyBqb87oZTCZnHEbJ1CCCHiQ4JuIYSIQjDVozOmMtlauSom66tocPPvLamU1jSys7YJrz/yunXNwvhhs2OyTiGEEPEjOd1CCBGFnqR69GTApWGa3L68mI+2lGOYitl5aQxPr2Jfo4dsp52xOWmhdihlMixjjAyiFEKIfkCCbiGEiEIw1aOzwDuY6tGbAZe3Ly9m+cY9WHUdXdNYU1kIQF56DTVNzQCBwBuddEs+U4d/L+bbKoQQIvYk6BZCiCiMHzabndUlKDrOsdY1nfHDZndYWzs44BJgzpj2s5xVNLj5aEs5Vn1/QK7QKK4cR0r1SAqzK6ho9DO3YDKThkxn2+YdMa2WIoQQIn7k01oIIaLgtGeQnzUO0zQi/t40DfKzxgH0eMDlC9+WYpiRg/pmI4UNVQWsLC/ki535OGxpPdwSIYQQiSBBtxBCRGlWwRGMzJmEhhaqKqKUQkNjZM4kZhUc0asBl3UeL3oXeeO6plHv8fV8I4QQQiSEpJcIIUSUdE1nzphFYQMk7VYn44bOxmlPB3pXWzvLYcdUqtPA21SKTIetdxsihBCiz0nQLYQQ3eS0Z3Q4A2R3Bly2tXhuIW+uLaOz0txWXWfx3ELoJLdcCCFE8pGgW4goVTS4eeHbUuo8XrIcdhbPLSQvw5noZg1Y/XV/d2fAZVt5GU4WTsgPVS9py2+aLCoaQV6Gk6amppi2WwghRHxJ0C1EF9rWTdY1DVMp3lxbxsIJ+Vy/aCaWCAGS6Jn+vr+DAy6D1UvaMk2DkTmTOqytff2imQB8vKUCv2mGtt+q6ywqGhH6vRBCiP5Fgm4hutC2bjIEBrMpBcs37gHgxqNkRsBYGQj7e1bBEQDt6nTrmh4acNkRi65z41GzQz399R4fWQ4bi+cVMiw9+Xv6hRBCRCZBtxCdiFQ3uTWrrvPxlgoqGtz9IvUh2Q2U/R3NgMuu5GU4+dVhU+PcUiGEEH0leZ/RCpEEOqubHOQ3TV74trRvGjTADbT9HRxwOWfMIqaNPCTqgFsIkZwsVz2L5apnE90M0U9JT7cQnZC6yX1L9rcQIhlVNtm5YNn0RDdD9HPS0y1EJ4J1kzsjdZNjR/a3EEKIgUqCbiE6sXhuIRa9857X/XWTRW/J/hZCCDFQSdAtRCeCdZP9ZuRpvf2myWET8pJ6UF9/IvtbCCHEQCU53UJ0Qeom9y3Z30IIIQYiCbqF6ILUTe5bsr+FEEIMRBJ0CxElqZvctwbK/u6v09kLIdoz7jkn0U0Q/ZgE3UIIEQf9fTp7IYQQsSWf+EIIEQfB6eyVIuJ09rcvL05wC4UQQvQlCbqFECLGujOdvRBCiMFBgm4hhIixgTadvRBCiN6ToFsIIWJMprMXQgjRlgTdQggRYzKdvRBCiLYk6BZCiBiT6eyFGFjG5aZLuUDRaxJ0CyFEjMl09kIIIdqSOt1CCBEHMp29EEKI1iToFkKIOJDp7IUQQrQmQbcQQsTRQJnOXgghRO9ITrcQQgghhBBxJj3dQogBw+1tYGvlKnyGB5vFwfhhs3HaMxLdrAFH9rMQQnSfBN1CiH7PVCary1ZQXrcNU5lomoZSip3VJeRnjWNWwRHomjzY6y3Zz2Kw2lbtYs5Vz0rZQNEr8ukohOj3VpetYHfNJhQKrWUmSE3TUCh212xiddmKBLdwYJD9LIQQPSc93UKIfs3tbaC8bhu6bon4e123UF63Dbe3QVIgekH2c2IFq+DUebxkOewsnlsodd6F6Gck6BZC9GtbK1eFUh06YiqTrZWrmD7q0D5s2cAi+zkxDNPk9uXFfLSlHMNUoXrvb64tY+GEfK5fNBOLLg+t421YqpfHT1nLU59eG/b6eYfekaAWif5IrlQhRL/mMzydBoIQSIHwGZ4+atHAJPs5MW5fXszyjXtQCvSW/a9rGkrB8o17uH15cYJbKISIlvR0iwFNqiwMfDaLA6VUpwGhUgqbxdGHrRp4ZD/3vYoGNx9tKcfaQU+2Vdf5eEsFFQ1uSTURoh+QoFsMSFJlYfAYP2w2O6tLUKgOl9E1nfHDZvdhqwYe2c9974VvS0MpJR3xmyYvfFsqEzAJ0Q8kfdTxySefsGDBAq688spOlzNNkz/96U8ceeSRHHjggVxwwQWUlZX1UStFspEqC4OH055BftY4TNOI+HvTNMjPGidPOHpJ9nPfq/N4Ow24IZBqUu/x9VGLhOh/kimOTOqg+9FHH+X//u//GDt2bJfLPv/887z55ps88sgjfPDBBxQWFnLZZZehVMe9MmJg6k6VBTEwzCo4gpE5k9DQQte8UgoNjZE5k5hVcAQQeFx/78cl3PLeKu79uISKBncim93vRLufRWxkOeyYXXyHmUqR6bD1UYuE6F+SLY5M6vSSlJQUXn75ZW677Taam5s7Xfbvf/875513HhMmTADgyiuv5KCDDmLVqlXMmTOnD1orkoVUWRh8dE1nzphFYTn8dquTcUNn47SnY5gmt76/SipA9FJX+1nE1uK5hby5tozOvvOtus7iuYV91iYh+pNkiyOTOug+99xzo1rO4/GwefNmpk2bFnotPT2dsWPHUlxcHPXOUkrR1NQEgNvtDvu/SJzuHosmTwOmaUa1XPB4i+gk03Xh8bpYV76SVbsraPLr6Np4TptVxLiceaFllB+a/E0s/aCEDzaXY7UEAmujJYrxG4p/lezC6/Nz3eH9Kyc2scfCEnE/D1bxOhbpFpg/ZkjYudua3zA5fGI+6RYln2UtunMsuhoYHA3Z732vO8etr+PIriR10B2turo6lFJkZWWFvZ6VlUVNTU3U7+Pz+SgpKQl7rbS0NBZNFDEQ7bGo8tXSaDR2WWWhylNLSW1Jh8uIjiXyulBKsde3ji0Nu3H5/CiloWkK09zGne//F+WbyPnT87DogeNf7fbx3ppdKMDbwXu+v3Y7h2X5yHX2v8f08hmVPOJxLE7MU+yr1Pi2ogm/aYae0lh1nbl5Tk7MU+2+t0T0x8Jut/fo/Wc6TweQfZ8gPT1uHYlVHNmVARF0B/U278ZmszFx4kQgcJdcWlpKYWEhTqeUYkqk7h6Lcd4Cvih9tdMqCxo63y08Eoc8Eu+WZLgu1u75iLKychoNha7v/wjTdRiT62Jn/XberMgP9Vw/+NkmUlJTOx2QZirFKo+TS+dNinv7Y6XtsfB4XeyoWYvP9GDTHYzJmS7ndx+J93Vxz3SodHl4aXUZ9c0+shx2Tp85mqHpUp6xre4ci82bN/d4PVOn9q8nYwNJb45bV+I9DnBABN3Z2dnouk5tbW3Y67W1tQwZMiTq99E0jdTU1LDXnE5nu9dEYkR7LFJTUxmZO5HdNZsiDqY0TYORORPIzc6LRzMHhURdF25vA3vrd1DnMToIonVGZtbxWdkeXMY08jKcNJlgs0QeVBtkAdxm++u/P0hxpLBx32ftymOWuzZLecw+Fs/rYmxqKlcvyo3Lew9E0RyL3qSW9MfPioGitylBkcQqjuzKgPgkTklJYdKkSaxduzb0Wn19PTt27GDWrFkJbJlIFKmyMDBtrVxFuaupk2cYoGuKMVl7eeHbUmDgV4Ao2fuJlMcUQohe6Ks4st8G3eXl5RxzzDGhGoqLFy/mmWeeYcuWLbhcLu6++26mTp3KzJkzE9xSkQjBKguHTT6TMUOmMzxrPGOHzuCwyYuZM2aR9PzFkNvbwNpdn7Jyx3LW7vo0rqUYfYYHv6noYjJy7BYjVLt48dzCUH53R/prBQif6aGyYbuUxxRCiG5KRByZ1OklwQ31+/0ALF++HIDi4mJ8Ph/btm3D6w0MjTrzzDOprKzknHPOobGxkYMOOogHHnggMQ0XScNpz5CygHGSiFk/bRYHVl1DQSeBt8JrWEI913kZThZOyGf5xj0Rp9P2myaLikb0y2m0a40dmFYTvZP+EymPKYQYrJItjkzqoLu4uLjD340ePZoNGzaEftY0jV/+8pf88pe/7IumCTHoBWf91HVLxLQGgDljFsV0neOHzWZz+SqavPUE1qTh8dsw1f6g01QaO+qG8/vjCkOvXb8o8MH78ZaKdhUgFhWNCP2+vzHxd5nfqGkaPsPTRy0SQojkkWxxZFIH3UKI5OTxuqKe9TNW04KbymTD3i/xGS7S7D78BoAixeLDa1hp9DnQMNnryuWgwoKwnmuLrnPjUbOpaHDzwrel1Ht8ZDlsLJ5XyLD0/tfDHaRjxd9FvrpSCptFqlwIIUSiSdAthOi2HTVr+3zWz2DPelpKNkop6j2N+M1AwJli8aKAbTUjGJb13Q57rvMynPzqsIFT6ivbMoZqra7TZXRNZ/yw2X3UosElODNnk6eBKl8t47wFUtVikHjq02sBOO/QOxLcEtGfSNAthOg2n+np07QGt7chrGc9w5lLqj0Tl7cBV7MX09Rw2lP5f0eeSkHOsJissz+w6Q6GZYyl0lXaSXnMSTF72pBMgk8t6jxeshx2Fs8t7LO8/LbjGUzTpNFo5IvSVxmZO1HKNAohIpKgWwjRbTbd0eVUvLFMa9hauapdz7rFYiXLmUOWc//66ps2wCAKugGmDv8etipbuwGtuqYPyPKYhmly+/JiPtpSjmGqUH7+m2vLWDghn+sXzcQSYcBsLCViPIMQov+ToFsI0W1jcqZT7tocmvXT6zepcHnwmyZWXScv3UGK1RKztAaf0b5nPdI6B+OAwWB5zGCqg8/wYLc6GTd0Ns4BOCPl7cuLQ5VoghMk6ZqGUrB84x4Abjwqfuk0bZ+6tBWP8QxCiIFBgm4hRLc57OnkZ41jV/Umyurc1Lq9oTJ+CqhqbCLFPoZF09Nisj6bZX/PulKK7TWN7da5r9HNluoqZo42497TmYwGQ3nMigY3H20pj1j6EQL11j/eUkFFgztuqSaRnrq0JWUahRCRDL5vJiFETMwqOIJN1RlUN3qB4IQ1CqVgT8MQXl83hNuXd1yuqTvGD5sdypHdXtNITVOgrmow7NEIlAr850ZHzNYpks8L35ZimJ1Xa/GbZmg20niI9NSlre6MZ6hocHPvxyXc8t4q7v24hIoGdyyaKYRIQtLTLYTokX2uZt7akIddz6IwuwK7xY/XsFJam0ezkYJFJ2a9jk57BvlZ49hetYFat5e2MY+GSYVrCIZyxr2nUyROnccbSinpiK5podlI46H1U5eORDOeIRly04UQfUuCbiFEjwR7HZtVChuqCiIuE+x1jEWZvlkFR/B56T5M1YCu7U8sMZVGhWsIayoLY75OkVyyHHZMpToNvE2lQrORxsP4YbPZWV0SGs8QSTRlGhOdmy66p7LJzgXLprd7/TzJIBLdIEG3EINcT0uv9XWvo67pVHqm8sWOtIg96/FYp0gui+cW8ubaMjqbD8iq6yyeWxi3NgSfugSrl7QVTZnGZMhNF0L0PQm6hRikevt4OxG9jlkOO26/vcOe9XisUySPvAwnCyfkh3qI2/KbJouKRsQ9UA2WYQyWaYRASomGzsicCV2WaQw+JdI1Da9hUu7yYBgmFotOfroDu0WXJzZCDEASdAsxSPX28XYieh2ToadTJFZwttGPt1TgN83QzaJV11lUNKLD2UhjqW2ZxiZPA9WeWg4ct4jcrK7rxNd5vGiaRmm1i1qPF6VaV/7xkO2wMzY3XZ7YCDHASNAtxCAUi8fbieh1TJaeTpE4Fl3nxqNmh9Ki6j0+shw2Fs8rZFh63x73YJnGpqYmSmpLcNiiK5GZ5bBTWtVArceHRngVHhTUuL2oqgYyZ3T8REcI0f9I0C3EINT68XZHonm8nYhex2To6RSJl5fh7LepFz+YNJxb/7WKjq4+Dajz+PjBpOF92SwhRJxJ0C3EIBSrQZCJ6HVMpp5OMXC0ntHTZnEwftjsuM0o+f6mvWQ6bNS4I1+HplLkOO28v2kv00bkxKUNQoi+J0G3EINQrAdBJqLXsT/3dIrkYSqT1WUrQoMig7Oe7qwuIT9rHLMKjghNzBQrdR4vhbnpaMGZVVvNtqppGrmpKYzNSZOcbiEGGAm6hRiEZEDiwNKXvbQDzeqyFaHyf8EJbzRNQ6HYXbMJgDljFsV0nVkOOwoozE2n2W9Q4fJgmAqrrpGX7sButUgVniQzLNXL46esTXQzRD8nQbcQg5AMSBwYlFKs3fMR1U07+6yXdiBxexsor9sWsd42gK5bKK/bhtvbENObmNY3vSlWCwXZ7Qdgyk2vEAOPBN1iwJNewMhkQGL/V+Ffh1nfgNVq67Ne2v4g2gmftlauCt2sdMRUJlsrVzF9VOymHpSbXiEGJwm6xYCViFzN/kQGJPZvHq+LRnMfqVrkYxWvXtpk1t0Jn3yGp9OAGwI3MT7DE/O2yk2vEIOPBN1iwEpErmZ/JAMS4y8eT1t21KxFtcyG2JF49NIms+5O+GSzOEKDFzuilMJmccS8rXLTK8TgI0G3GJASlaspRGvxfNriMxPXS5uMejLh0/hhs9lZXYKi4xHFuqYzfljHM7P2VjLf9EpqnhCxNXifrYsBLZir2ZlgL6AQ8RJ82qJQEZ+2rC5b0eP3tumBXtrOxKuXNhkFJ3zqTHDCpyCnPYP8rHGYphFxedM0yM8aN+gCTVOZrNyxnI83vMiOqrXsrdvKjqq1fLzhRVbuWN7lZ6sQIjLp6RYDUiJzNYWA7j9tiXbwX9CYnOls2vNtp22Idy9tMunphE+zCo4AaPc0Qtd0RuZMCv1+MJHUPCHiQ4JuEVeJejyZyFxNISD6yhhbKlby2rrMqAf/BTns6aTpQzFVA9A+sDdNg5E5kwZNL21PJ3zSNZ05YxaFfVbZrU7GDZ2N054e72YnHUnNEyJ+JOgWcZHoyiHJkKspBrdon7Ys37iD5RuHRD34r7U86zRUZmW7Ot2DsZe2txM+Oe0Zg2bAaWcSVUZRiMFAgm4RF4l+PBnM1Qy2oa3B1gso+l40T1uafQab9nm6NfivNU3TmDZiIZrVGPS9tFL7OjYkNU+I+JGgW8RcXz+e7CgXVnI1RV/o6PyL5mlLRaOXLdWjOn3/4OC/zipcSC9tQCJqX1c0uHnmy01s3lnJxBor5x5U1G8C+0jpf5KaJ0T8SNAtYq6vHk9GMxHGyJxD+Lg0F79vE06bybzRI5g1+oBB1wuYDLo7UDDZRXP+dfW0pdE3BJ/ZefASafCfiKwva1+3Pv5ev4GnqYl1Dbt5d1N5p7n4yaCz9L/ctJFodDUgVVLzhOgJCbpFr0TqKemrx5OdTYTx/oY9fLilHA1aAqIcTKV4Za2XhRO2JPUX4kDT3VkC+4toJmL57Q86f9qyqW4EptrZ7cF/onN9Ufu6uxPxJJPO0v8qG3agaxZM05DUPCFiTIJu0SOd9ZRomgXTNNE7CaR6+3iyq4kwdtU1UeP2Mi0/ixRr4Iujv3whDjT9OTjpSLQTsVw8v7nTyhgjc9y8uXZXjwf/icToyUQ8ySKa9D8U5GWOpcq1S1LzhIghCbpFj3TWU+LzN+HxNZLuyOnw73v7eDI4EUakHkKvYVLr8YJSVLg8FGSnhf0+mb8QB5r+HJx0prPzL6h1LnZHOdfJNPhvoKX/xFN3j38yiSb9T6Fw2jM5bPL3Bv0A3Y6cd+gdiW6C6Ick6Bbd1lVPidViB18jPn8zNmtKu9/H4vFkZxNhlLs8KBW4Cehohrpk/UIcaPpzcNKZnk7EEkkiBv+1NlDTf+Iplse/r3Un/U8G6HbMctWz3VreuOecOLVE9CcSdItui6anJNWehdViQykzLo8nO5sIwzBMNAIpLBY9chuT9QtxoOnPwUlnejoRSyR9OfgvkoGY/hNvsTz+fU2qkwiROBJ0i26LpqdE13XyMscyefhBcXk82dlEGBaLjiLQW5OXHvmLI1m/EAeavgxO+jI9orcTsUTSF4P/2hqo6T/xFo/j31dk4jAhEkeCbtFt3ekpidfjyc5yYfPTHVS63OQ4U0KDKNtK1i/EgaYvgpNEpEd0dv41+w32NrgZm5POC9+WJnVudKzTfwZLXnhPc/GTYf/IxGFCJI4E3aLbkqWnpKNcWIfVwrzRQ/D5jYh/JzPT9Z2+GCiYqPSItuefBpRWu6j3+Mh02FDA68U7QsH/lYeNY3vV6rDymokObGKV/jMY88JbH39fy12lqRQ2TWuXi59s+0cmDhMiMSToFt3mtGeQ7iigZO86/GagpzIv3YHdGvjS6Kueks5yYXNTU7h9eXHCBqfFS6S66IkO3LoSz4GCiUyPaHv+LVtbhq7rTB2ejd0SaI+maaAUlXWf8+yX/6YwJy2svGZ+1jhmFRyBriUmII1V+s9gzAtvffyf+XIjW3buYWLBSM49aFK7XPxk2z+6pndaylIIER8SdItuCfbYfLxFY9rQVPLTa9E0xb5GD9lOG4U5mX3eU9JRLmwiB6fFWmd10RMduHUlngMFk6E6Sl6Gkx9MGs6D/9mAUopyl4f8dEco8J4xrJQRGdXUuXV8manYrVqovObumk0AzBmzKC5t60os0n96e+PTH28kW8vLcHLpgkmUlPiZOnUiqantU0qSNW9eqpMI0bck6Bbd0rrHZk3leDZVN1OYXYHd4qe01kJV80x+O+ugRDczJBGD0+Khs7roiQ7cOhIpfzXWx6Kj9Ihmv0GFy4NhBirY7K5riul6g4I3oc99s5WKBk+g9xKoavSQ7bBTNMxGfnoNisDg3gqXh9HZqaG/13UL5XXbcHsbEhJoxiL9p6c3Pv35RrI7kuHGUMTe46esBeCC16YnuCWiP5GgW0QtUo9Ns5HChqqC0M9bauq5SCodxFQ0M8glMnBrqy/zV9umRyil2F7TSK3bGxrsayrFG2vKSE+xxjx3NngT6jf2t0EDUFDj9pJl34umKUBDIxBctWUqk62VqxLW49jb9J+e5oX3xxvJnhioZTOFEN0nQbeImvTYJEY0ddETHbi11pf5q23TI7bXNFLd1IyuaaH9pes6w9IdMV9365tQi67tD/IJzIqKUvjNZgwTLDooiNibHJyIJFF6m/7Tk7zw/nYj2Rv9uaa3ECK2+v+zO9FnpMcmMbozg1yidSd/NRaC6RF+06TZb1DrDj9HFZDtsGG36DFfd/AmFCAv3YGmaXj8Bk1ePz7DxG8qXM0ajV4fHp+B1rJcW8kyEUkwFevGo2bxy8OmRp1vv3huYYeTUAW1zQsP3kh2Jngj2d/1ZP8IIQYm6ekWUZMem8To6QxyiagJnIinIcH0h+e/2RY6P4OTI+U4bIzN3V+NIZbrbn0TGqwH7zUC6RnBrf9qVxZzRjTgM00cWEIVflqLtrxm2+N58pT8Xm9DLPQkLzzRN5J9eW30RdlMIUT/kNRB965du7j55ptZtWoVqampHHfccVx11VXobT647r//fh588EGs1vDN+eCDDxg6dGhfNnlA606lg2SYBKIr/aVqQnfroieyJnAinoYE0yNczX5eW1OGYZhYLS1lLC3h26lrGnvqmrj345Jen5utb0K9holCYbfo+IxAD66madR7rGyqSmPW8MBATq/fDAu8oymv2dHxfKN4O5PTFHdNnhLx7/ryGuxuXnispyKPdlsTdW3Es2ymEKJzyRRLJnXQfcUVVzB9+nSWL19OVVUVF198MUOHDuX8889vt+wPf/hD7rjjjgS0cvCIpsfmiEnDefjzjUkzCUQk/a1qQndnkEtkTeBEPg0ZmZXKqExnh+tWQGlVAzvrGhmZmdrrc7P1TWi5ywNoOKwWbLqGz1QoBTarxs76SRRk7SYvvYYKl5vR2Wndmoiko+NpmIov9zZy10frufX474SWT0Rg2d288FhNsNXdbU3UtRHPsplCiM4lUyyZtEF3cXEx69ev58knnyQjI4OMjAzOO+88nn766Yg7SvSNrnpszJYvr2SZBCKS/lg1IdoZ5BJdE7gvpn3v6bq3V7uo9fiYlp8Vk3Oz9U2oYZihlBKLrmPRAzcXuakppDtSKK4cR0r1SH4w0c3wrNyoJyLp6niapuLvK7fjNQM3HYvnFvLw5xsTdg1GW6IzVlORdyeITvS1AX1XwrQ/PGkcbCxXPRvxdeOec/q4JYNLssWSSRt0r127llGjRpGVlRV6bfr06Wzbtg2Xy0V6eviX1YYNGzjzzDPZuHEjI0aM4LrrruPQQ7tXyUEpRVNT4DGw2+0O+78I8HhdnDipmu8VeFizt4l97lFkpGRy+szRKOD8f/wXjUAPVFsa8OHGPZw7p4BhEQaUdSSWx8LjdbG7ejMKMIzI08Tvrt7CmMxZOJJsZraioQsYkzmLHTVr8ZkebLqDMbkzcNjS8LgDua/PfLkJr9/otKfZpxTPfLmRSxdM6nYbujoW6RaYP2YIH2wux2qJ8DTEMDl8Yj7plv3XWqx0tm6fYVDd1Ey2045V19qdnz09N3+9YCJen59/rNqOEcwnb0mbyHbaGZ3lDK3LZVgx1GSKhgb2u/JDk7/zfdDR8VRKBW4i3F7QNF5dvZ0RmU5eWrmN3XVuCrJTY3oNxsPEIQfj8/mobNiBqYxWN5IWhmUUMnHIwZ2eI5UuDx9s3BP15008r41k+b4wTMVdH63nP6WV+NukIh1SOIzfLJzS5aDO/q47x6KrFKdoBOt1Q89qdsf6c3Aw6M5xS0Qs2ZmkDbpra2vJzMwMey2402pqasJ21PDhwykoKOCqq64iLy+Pv//971xyySUsW7aM8ePHR71On89HSUlJ2GulpaU934gBRClFhX8djeY+VEtPa55dkZ+yizR9KBU7vLy4oZp6l6vL1IL73/+axVOGdLsNsTgWlb6NuAxXl7mk/13/b4bZinq9vvjIRCcTA9hWvSPsN5t3VuKJ4kN8y849lJT4e9yCzo7FiXmKfZUa31Y0tXsaMjfPyYl5qt11FisdrXuf20+qBYbaFE2NjRH/tqfn5snDYWZKHr/5ZCf+lsl4clMs2CzgbnUsdE1jtsPdrW3v6Hjudnlp8BrQMs28u9mLp0lR3uSjxuPH6/UyMt0e0+2MB508cs1Mao0dmPixYiXLMga91sGG2g2d/u0L66u69XnTF9dGor8vHiuu4Mu9jVgj7JO3i13sq6zgwpl5CWhZ34v2WNjtka+TvhKvz8KBLtrjlohYsjNJG3RDIPiJxumnn87pp58e+vm8887j7bffZtmyZfz617+Oen02m42JEycCgbvk0tJSCgsLcTrlsdzaPR9h1jeQqrXfF6ZqQGVW4sweRnpN52XAAFKzc5k6NfpHrLE8FuaeCoyG2i6XG5KRzdQRvXsM7PG6wnulc6bHvfd8Yo2VdQ27uwxEJowewdSpPevpjuZY3DM90BP50uoy6pt9ZDnsnD5zNEP7oHc10rp31TXyv911Xf5td8/NoKlAcbMzYi+712+wt8HDmOxUVnmcnF4UfS/zpBofpJRhtxr4DAtba/JoaLbSVO9Dt1gwTRNd13Gm2ElNS0X3NmK1QpMJ1hQHdmvkOtg93c74mdvtv3DuXdetz5t4XhvJ8H1R6fKw4fNKMtM7/ozZ6IKhBeMS/pQjnrpzLDZv3txHrepYcl2H/UN3j1tfx5KdSdqgOzc3l9ra2rDXamtr0TSN3NzcLv9+1KhRVFRUdGudmqaRmpoa9prT6Wz32mDj9jZQ3bQTq7WjwW8Wqpt2MiwjH03TuvxSG5KR1qN9GotjkerIQG/Uu+zpTnVk9HhdHQ3ULHdtjvtAzXMPKuLdTeWd5lTbNI1zDyoiNbXnwUE0x2JsaipXL+r6Wo2Htuu+9+MSvt1TH7dzE+D3x87Fvrw4NN5BA0qrXdR7fGQ6bOgWnbc37OXdTeVdDmgMnkNFQ7bg81cTSJZQjMmqZuO+VDZUDIGWc0jXNIZnpmLRdWxWC+ADpdjX5KUgO63b29lf8oGHZqShaVVRH9O+uDYS+X3x+tfbUZqGpYv98fr68kExeVk0x6K3qSWxMNjji57oznFLRCzZmaQNumfMmMGePXuorq4O7Zji4mImTpxIWlr4F8mDDz7I3LlzmT9/fui1LVu2cNxxx/Vpm/taX305Rjsj4kGj61i2VkvIILpoxapqQmcSOVBTagJHFs8Bnm2vw3tPPoD3N+1l2doydF1n6vDsUOlCLcoBjcFzyGa1kO1MoabJi6ZpaJqiMKeORRMN3t8yHAVktkz+A5Cf7qCqMVBJJThxT7TbmchSkz3R3WOaiGsjWJa0pslF8Z5GqjyjyHBkRf1Z3Z2ypjJ5mRDtJVssmTyfoG1MmzaNmTNncs899+ByudiyZQtPPvkkixcvBuCYY47h66+/BgJ3LTfffDNbt26lubmZJ554gh07dnDKKackchPixjBNbn1vFT957hNeL97BR5vLeb14Bz957hNufW9VxEFFvRHtRBYpViM0O2AkftPksAl5CQ34glUTTDPyIErTNMjPGtfjet3dmd46Xq5fNJNFRSPQNQ23z09ZbSNbqxvYVdfEwWOHDsqawK1nroykJ+dmR9fhL1//it11TaAUY3PS2tUKh85n5mx7Do3NSSMnNZC/GIgvNcbnNpBu95FptzAme39Pmd2ik91StjHSgLnOtjNYCUQpIlYCuX15cdT7pi/05Ji2vjbMlmg9WOIylvWyTWWycsdyPlz/Ap9s/pJPt6yktmkjFuMjtpZ/xFnPfdzpZ3Xw7z/e8CI7qtayt24rO6rW8vGGF1m5Y3nE2TyD5To7b5dMXiYGl2SLJZO2pxvgvvvu48Ybb+SQQw4hPT2dM888k5/85CcAbNu2LTTq96qrrgIC+Te1tbVMnDiRp556iuHDhyes7fHU17VmuzORRX+YBCLa8ns9Ee1Tga2Vq5g+KnYjoluz6DrXL5qJq/lb3lhTht8wsVstDElL4Zud1dy+vLjPey2TYSKiWJ+bnV2Hr6zegWmajBvSfhu9hkm5y4Pfb/CLl7/kr6cdFBYYtj2HNE2jMDcdr9+kwuWh2W+wr9HDMZPcrCxLbXeujc1NR9NgeIYzFFB2tZ3JUE6vJ7p7TPuqXnbwScWOWjc1TT40DTQ00BQjM6rR0Fi+MRAgR/qs7snTskSW6xRdk9KAiZNMsWRSB93Dhw/n0Ucfjfi7DRv2j2xPSUnh+uuv5/rrr++rpiVMIr4cu5OS0R8mgdA1nTljFoUFgtHWTe5Koqe3Drp9eTFfbK9kVFZ4vmBf10tPpomIYnludnUdKqWo8/ho9huhKeIVwVrhXpQKZGl/u6uKnzz3SVj6RkfnkN2qM7qlV9uia1Q2Rn5aY5gmPz1wAhfPL4p6O1/4tjSUUtIRv2nywrelSZUP3NNjGs962cEnFX5To9btpe0uVejkpdeQVj0y4md1d56Wtb5xldSyxOhJmUDRt5Ipluxx0N2651Mpxfr16xk5cmRYLUQRe4n4cuzJRBZ9NQlEbzjtGTHvbY719NY9kUy9lsk4EVEszs2urkOLRcdUigqXJzSYcXu1ixq3F42WYZEtPbJtb4SiOYfGZKfS4M3i6+2Bnl0LtOvhteh61NvZ3/OBk+XzptLl4Z31H6CpelzNRoezs+qaojC7gpJ9o9t9VvfmaVl/eNIoxGDWo6D7m2++4ZprrmHFihWYpslPf/pTvvrqKxwOB3/961/DktBFbCXqyzGeKRkDSV8M1OxKLG/M2g4SPHlKftTt6GmPXX/Q1XUYHNAYHMzoNUxqPV5a/4WmaeS1lG5rfSMUzTlk0S1ccOCRzHFuZZXHidvUevVEKZgP3FUlkI7ygftLxZPu6M42GabiseIKNnxRycz8Skak+3D7DAxTYdU1HLa214CG3eKP+Fndm6dl/eFJoxCDWY+C7rvvvpszzzwTgBUrVrBp0ybef/99vv76a+6//34JuuOot1+OPRXPlIyBJFbTW/dGLG7MOqpk8UbxdianKe6aPKXLdiQqv70vAsCurkO7RScrxRZKLSh3eUIpJbB/iviUVnW0W98IRXMOOezp5DptXDpvUq/LjvU0H7i/VTyJhmGaXP92YDyE129gtVoYlpbS6Tbd9dF6vtzbSGZ6Oj7DBih0DQwUfhM8PqNN4K3wGtaIn9WxeFqWLD3/QohwPQq6N27cyNNPPw3ABx98wHHHHUdBQQEjR45k6dKlMW2gCJfowTLxSMkYaBL9VCAWN2YdDRI0TMWXexu566P13Hr8dzptR1/nt/dlABjNdTh+aAbfGZ3L/3bW4PcboZQSTdPITU1hbM7+clXNfoMKl4dla8oAOHNOoOOis3PI447duICe5gP39aDueDNMk6MeXs7/dlZhmgq/qVAKdtU2MjQ1Ba8/UDWk9TZVNLj5z7bK0CyQpbV5FGTuw2YBnxE4QfymwlQQLChjKo3S2ryIn9XJ8LRMCBEfPQq6LRYLFkvgrv3zzz/nhhtuAMA0TXy+5Mz5GyhksEzyS/RTgd7emHWZE65pfLatssuc8L7Ob+/rADDFamHt3lrsVgv56Y6w0oDB6zD4qP8XL3/Jt7uqsOk6een7Z4pUSrG9ppFatzdQ7k3TeL14R+hG4crDvsv2qtV9cg51Nx84mcYOxMr1b/+P/5VV4TVMfEYgwNY0DWUq9jS4cXn92C06F8/fv00vfFuKv9XF1mykUO7KZkRGFVZdw2eaaGj4DJMUq46GSYVrCI0+G4uK2pdvTIanZUKI+OhR0D1t2jQeeOAB7HY79fX1oXSS9957j8LCwli2T0Qgg2X6h0Q9FejtjVk0OeE+pbrMCe/LHru+CgBb96b7DROLplHl8rDP5SbHmUJBThq2NtdhXoaTv552ED957pN2N0LbaxqpbmpG1zR0XSc/3RHhRqFvzqHu5gP314onHalocPP6mjKa/WZgRtFW2xX8d0Ozj037GsK2KVI615rKQgDy0mvAH0gxUcrEVDrlrlxK9o1jUVF+h5/ViX5aJmLPctWzMXkfKT3Yv/Uo6L722mv5f//v/1FfX8/vfvc7nE4n1dXVLFmyhD//+c8xbuLg1lF9YxksIzrTmxuzWA3W7cseu74KAFv3plt0ncLc9FBqiM8wSbNbeGrxIe2uw0g3Qs1+g1p3YF8rIKfVzJKQuJ7iaPOB+1vFk65y/V/4tpQmr79dwB1G09jX5GF3XVPo/b7YXsnO2kYyLPvvqBQaxZXjSKkeSWF2BRpeHDYnuSnTmJCfwQ3Htv+sbvtZP3n4QUweflDUT8sG4mBWIQaaHgXdU6ZM4Z133gl7LTc3l/fff58RI0bEpGGDXTT1jaP5cuyPH8S9qZghAnpTxSCWg3X7qseuLwLAjnrTU6yWUFlAr191mNbT9kaowuUJ7GddJ8dhY2xu+2Aq0o1C8Jjua2jEXVvNFQUexvZyIGVPJGpQd3dFm+tf5/HS7DdpyfKJSANMU/HWujK+2VmFYSp8hkmV28c+wyDHrzFuSEZowGyzkcKGqgJ0TeP5sw+N+Nnb1Wf9iOz5vLhyR8vnYVm7z/CBOJi1P3n8lLWhf0vNbtGVqIPuTz/9NKrltmzZwqGHykC73uptfeP++EEci4oZ8dQfb2B6UsUgmpxwm6ZFNVi3r/Lb+yIA7G1vetsboWVrykJlAyNNFQ/hNwptrw+lFC6Xi/+99F8OnzSiz6/pWA/qjtespR3l+nt8Bk9/tYUPN5dz0owCdMBhs6DrnYxBAEwFDR4fwzMC75NitZDtsFHt8lPr9rK92kVhqxuortK5OvqsN5XJF9tWsr5yC6vKC0Ofh++t38yiCU0snJhLitXJ62utLN9YO2AGswoxkEUddF944YWhO/DOaJpGSUlJrxs2mMWivnF/rCoQi4oZ8dAfb2B6o8uccKVYMG5Yt2444p3f3hdVfWLVm976Ruj14h1R3yi0vT6M0BTvibmmYzWoO56zlkZ6OtF68KpSihq3D4/fwKprNHmNQKk/s4PebgW6Dg5b+FfnmOxUfF4vTaZGjdvLSMPEqmtdpnN19lm/vaaRmiY/eWk1OK0j8Rp2ZueVkp9ei8dr8t/SXQxNTcHXXEdhVgZflI1iWLozrAxlfxzMKsRAFnXQ/e9//zue7RCt9La+cX+sKhCrihnx0B9vYHqro5xwC3DQ8DR+szCxTx3a6ouqPrHuTe/OjUKyXtOxGNQdz1lLIz2daD14NRjgVzY2U5CVisOq0+TVsFkIlQvUNALHSAO7Vcdu0UOTGgVpmsbIdDvWFAeVjV4yUqwcM2VUWDpXpCdllfWRP+u9fjM0hbxGYPZKu+5nREYVCh3Q2FPvZmdtE4ZSjM6sYVa+jxVbR5LttDM2Jy30nv1pMKsQA13UQfeoUaOiWu6cc87h2WdjM0p3sOptfeP+WFUgVhUzYq2zYMdrmJS7PDz91RY04Ofzi5LmJqa3OsoJP3lKPpVl27B08gg+UeJd1SfWvenduVG49+OSmFzTsU7h6O0MiPGetbTt04nWg1eDNMBoKQ9YlJfFql3VGEqF6mwrAvt9aFoKKLDoWlhvcmt2q4UxOWnML8zjly3HobMnZSdMrmDy0PbvU+Hy0BLnAxpOq4ehqQ0tATe4mlsGfLb83lQwLqcRq+6hqjGwLcEUl2QazCrEYNejgZQAf//731m5ciVerzf02t69e9m4cWNMGjaY9ba+cX+rKgDJ2+ZINwMKqGyoZnxOBSPTDDw+Cy+t9PHhlvIBl27SNie8qamJygS2pzPxngI7Hr3p0d4o9Pb6iGcKB/R8BsR4z1ra9ulEhcvT7rNVAZaWnHoNmDkiG6tFp6y2CZ/fwGa1MDQthVSbFbtVp7G588+gtk87OntStr7CjcPiojA3/IZif0AdaGFGihtNC4ThgenlA7W/99Ow6wZnzNjFngYnPtNKo38MitSkGMwq2pPSf4NTj4LuP/7xjzzzzDNMmTKF1atXM3fuXDZt2sSoUaO44447Yt3GQae39Y37S1WB1pK1zW2DHQ3F6IyNHDyyDl0L9kUpZo9wUd88hH9vNICBl27Sn8RzCuxY96ZHe6PQ2+sjnikcvdHbp3pd9dy3fjqRYmlm3og9oJpx+y18uyeHRq8NTdPIb5UuYtF1Fk4YzsXzJ7U7JkoRsd56a62fdnSVFrSjbjhjs6sZ6TexW8PLRQY/XUyl4fI6cVj9mEq1TNoTPIaBDpjcVB+pNoMsn590eyAv3WpxUecZwrp94+I2Q7EQont6FHS/9dZbPPfcc8yYMYNZs2bx/PPP09jYyPXXX4/DEZvZ5Qaz3tY3jtegsnhW74hlxYxYahvsTB26jWxHLUpp0KqnyaIrRmRUAfDxFmtS5cv3VKSABiI/Vh8s4tWb3tWNQm+u6XincPRGT5/qRdtzH3g6MYzKui8ZkV6H1/CFpmafnlfPlup0vtldEFY9Jnjz0tEx6fRph2FyZKunHW2flAVruhumwqIHKtfsbchiWFoTo7P3VzzJS3ewr9ETmr3Sa1qB+tAsmUGappHr9OK0+dHQMFWgTQoNDcXw9CoKh6T1+88iIQaKHgXdVVVVzJgxAyD0YZeWlsbVV1/N5ZdfzhtvvBHTRg5GHdU39huwsyGHr8uH8FFpScTAN9aPwfuiekc8KmbEQtueslxnNYbZNkDQsFl0FBp56TWsr3LHJfe8r0oWdhbQ5KaOpsadzYOfbaLJpN+UToy1ePamd7S+nl7T8U7h6I22T/VM08Dtc6GUiabpWPRUKhv9fLPXyvIt+z/vutNzf/zkffy31EWtW2GzWPAZgadRugbT8lwMz9jHmsr9NxtddUh09LRD1zQOnxg+y2TwSVnbiinBa6qqsZm99cMozHWj4Q4dJ5tFI8tpp6QilXWVhdgtXgoy96HU/qBb08CimaTaDALP4KCh2RL6t6ZpZKc6KMxxh91Q9cfSp/1F65rdHZFa3oNbj4Lu7Oxstm7dyvjx48nKymLz5s1MmjSJ/Px8duzYEes2DkrB+sY7qit5e83HeHxuSmu8lFQOxWemoGuVnQa+sXwM3lfVO5KxYkbrYGfykAo0wnuaFApb2H5RjM+ppN5TGLM29HXJws7qBn9dtobiXSmsqZ6EzWJJSOnEZA0a4t2uttcHELo+vt/JNd3bFI54av1Ur8lbj9fvDiXVefwGfqOWqqYMPiytw1B1vLm2jMMnZDJv+Naoeu4BKutLKczNxOsPTEi0r9GDx29gt1jQNchPr2FT9UiajZSoOiQiPe1ItcBsRxML5k4NuwaCT8p2tKmYAoRSRXbV+3ns6wz+edHRNLg3hGrZf69oJn/6eBvavgrcfjvlrmxynZWhUoa6ppFh94EWeObm9lkxlR4IxnWNGSOySbNbUS03VFNGLOj150i8aqkLMVj0KOg++eSTWbx4Me+//z6HHHIIv/71rzn11FNZtWoVo0ePjnUbB6XwQCudHTUaVU0WLHoT2Q4/Y3PTOw18Y/UYvC9LlSVrxYxgMFNdv72leK9q+U/Dpus4bK2//DVsui+mued9WbKwy7rBbj8jMr1sqffiV84+LZ2YrPXS+6pdba+PqoZG3LU6V/zgAMbk5Xb4d70dmB1vswqOYG/tVpp9biDQTrffj99QeA0bFt1gxrBSiivHoRSUVRczJKWh3eDD1oI998F/a5qG3aozOjuVUVnO/b3OBG6UC7PL2VQ9tlsdEq2fdjQ1NUWcn2Lx3EJeXlUaVjFFKUWzYYZSRTRNY3ddE+f87ZuW9S8InS/hn4f5ZFrX0OjZidfw4zcDbUdpNPkt1LhtoXUMz3CSZreG3t9neHr1ORLvgbhCDBY9Crp/9atfkZ2dTXp6Otdeey2/+tWvuPfeexk7diy33HJLrNs4KLX+gPQZgZqtFk0DBTVuL7Sa9ayzwLe3j8ETUX6wrytmrNtTw43vrqLW7SXbaefWY2YzbURO6PfBYOfLrdWsLHOxp97T8ghYj7BfFIayxSz3vK/rM3eUihCqG4yGrivG51SwsXps3NoRSbLWS+/rdgWvj2CgNzS982C5twOz463Z19iSCpGHx+/CZxg0ejXcPiuqZQxBXnoNKdUjafDaaPZ72NvgATRGZqaGDUAMat1z3/Zc1jSNwtz0UM+33zTJTk3j5uMPjUmVm9byMpykWHTcPn9LOwI3ZIYZuMbS7D7mj64jPUVh0Wv5ZEsztxN+voR/Hs7htve+YG9dMW6/h0avi4wUL34zuA8U6SlWxg/Znx+ulKLZb4n6cyQjxd+uN3vD3i+TciCuEP1Nj4JuXdc5//zzAcjKyuKpp56KZZsGvbaBVtsyVxpQ6/HhNczQAKB41d1O1lJ+seD1+zn20RWs2lndanY/xUeb9zJ7dC7/vOgI7Nb9l8is0QdQ27gZhUZNkzfijHWmqTEie3rMAs++vunpKBUhWDc4QMNmMeLajnbrT9LJYZK1Xa31dmB2vAVv9CwWKzaVybbqehqarega2CyBc1/XFJn2Mr4syyU3RTEqw2Bvgzt0o9x6MhgI77nvqJc/2POtlGLMkIKYBdzBnuladzNf7tjHpsoG0DT8ZiDYDvauH19USdHQRiwtVZB03cV0ax376vdSXj+R/My0iO9/7aLvcvtyJx9tKee/O/fxwylbWibRIdQRsL2mMbRPdE3ny51ZGGZlp58jhmnwyrcvk5e6F9M00HWdFEsa2yuLafLVk5aSHfHvEjkQtz+zXJXY+UykZGFi9Cjofv311zv9/cknn9yTtxUt2gZahtn+S0MpRbnLQ0FWKtB14NvTfNNkLeUXC8c+uoJvyqqwaFp4DyXw1fZKZvzhTc7+zvhW+ysQvPiNQM9O8PF0MOFER5HqKOCK7383Zm3s65uejlIRgnWDA4G3wme0D97iefOViCcu0eSv9peJqDoamK1rOiNzJoV+3xO9zWUP9kiXVruodXvx+AwMU2Gg8BkKqx5My2gGBd/uyWF6Xn3wZKSmKTBXRPDJH4T33PdVL79hKpZ+UMLnO6owzP153F7DBAVOm6WlxnYg4J46rBGltJZKSGCa4Pb5yXZU8uq3y/jFwsWh9267jy+eX4Sr2UdptYs9rmwm5NZh1S2h8zC4T8ZkOxmZM4kv91g6PUc1FIvGrcROIx6vHjg//AqP1tRStjCQCpPuyIn499EMxJV8cCF6GHRfe+21kd/MasXhcEjQ3UttAy2LroUCIcM08bVMT1ztaiYvLYUUq6XDwLe3+abxKj+YaOv21LByZ3UgZaeV1vmWpVUu3lm3C6fNEtpf1x75fQCslm14fH4qG5vxmwYW3cr0EZM5eMJRMc1t7Oubno5SEYJ1gyHQm7+1Ji+u7WirL28+upO/2l+eBAUHZrcOfOxWJ+OGzsZpT+/6DSKIVS67zeJge42LmiZfoMc2NG4isF99polSCo8/cKPX6LWxpSqN6cMbgcAwi1q3F29LrWvTNMhKHccjX+ykzuNlmCODkRlVpFjbn5ux7OV/cm0lxXWKFJsVv6mobTk37BYLjV4fmgEWTSPd4WPy0CZM1bro6P40GJ8BNU3bcXsbsFvTIu7jV1dvZ2ddE2Oz09jVUESOo5S89JrQftM0RU2Tj5kjpzGr4Ag+Kt3Q6efI7PzN5KY2oGnh6SMAfsOHpml4/W5MMzPi05LOBuJKPrgQ+/Uo6F69enXYz4ZhsHXrVh555BHOPffcmDRsMGsbaOWlO9jnCoy4D9Vp1TSa/H5KyuvIdtoZl5seMfDtbb5pPGbhSwY3vrsq4pdQMODWWvbRtmoXM4Znt9lf+4OXsTEIXjrT1zc9HaUiBOsGg8nu+gyajRQsbU6HeN589eXNR3fK0fW3J0FOe0bMygLGKpc9wzmZGvd/QulaNoseVo9aKfCbGl/vygq99sXOIUzP95JqayIwS6ONCpeb0VnpbKzK4O3PNPzmjpb2ZDMrv47Jw1yMzU5F1/WY9fIHVbo8/K+8CWdaICWk3OVBtQTVgTSZwDZlpfg5c9ZuhqY1Yyqd+mYLpqmH0kNathifYbB659e8tzk34j7eVe+mqjHQ81+Ym05x5ThSqkdSmF2B3eLHa1jZWjOM1LSxHDJJ7/RzJMXSzJisfQBh9cpDNA1DGejKgtvnIi0lq90inQ3ETdaJmYRIhB4F3Xa7vd1r06dP58Ybb+SCCy6QOt2t9OTRa3DEe3nD/kkUFIHBbHqrCh4p1kBQVNXUTOGQ9HbvG6t801jPwpcMWlcTSLP7mDuihhSLQX2zxle7snB5bWhaYLKLoPD9FbvgpTOJuOmJlIoQrBu8dq+T/+0chiXFYF+TO3R+Dkm1c9y00XG7+eqrm4/uTiTTn54E9eSzqKO/iWUu+zNf72ZLZSqFOXUEa95b9UAONICmKTZVpVHrseK0Ko4cv5fJw9zYrRoefwoOqxenzYfPr/PN3pm8t7EWq64R/KjUNJ3iinGsq3RzbJGHwycNifmN8kuryzBanQSGYYb1YjusOsdMLGfikEaGpjWTajMBk3S7H7fPQq2n9XdqYB98UbqbL0pdTB9Wgd3iw2vYKK3No9lIwTBMLJpGrdtLs98gxWqh2UihuGIU5S5P4PcWkz11TUDnnyOF2RXomonVohOpQJSOjomBiRFWJzxsmQgpOhUNbl78tgSbWo1N18lLd7Qb9DoY88Gllvfg1qOguyOZmZls3749lm/Zb/X00athmjz8+Ub2tPRk6JqG3zBwtwq4lQKbRQt8qGsaQ5x2NGj3BRerfNOqxmYyHXbmjc5l874GJg7NYFRWaq9n4UukbKcdpUx+MLGCCbkuwESpQN3t2cMb2FiVxjsbhmFt0/OTiPzcvr7p6SgV4XtFM1m6fCO7Gzfjqm9AU4pg96SpFKYKnL/xKNvXVzcf3Z1Ipj88CerJZ1FXf5OeYuv1Z0twHU9/tYWaxiEsmmgwIbcBn2Fi1XWsOngN2LAvg3c2DsWiKY6cUM7UvEZAw+NTgRtjMwVN08hx+qisX4VVHxd5fcrJOxtTOeu7h8T8eNQ1+8JTAi16qwQZWDShnKKhjeia3jJrZGBCGw1Isxtomo8at71V3X9o9OzikIKd6Fpw2ndFQeY+yl3Z7KzLC6R7KUWFy8Po7DS2V7uo9XhDPeymUry2ZgdpKVauXzQz4ueI2+en2deEQkfDxFS0C7x1XUc3dQzTwGt4aGyuw2lLD92Ytk3RaX3uFOXuYExWMwqNfY2eiINeEzUxkxCJ0KOg+9NPP233msfj4Z133mH48OG9btRAEOnRq99U7Kp38/iXm/li+z6ePHNBuw//4N+NyUlHqUCVEn9LB4pSYNU1Uh1WMlNs2CwtvQcWHcNU7b7gos033VPXxL0fl7Trzeroi/ebnV7SU6zkpqbEdqf1oVuPmc0taiUThtRjmoFSXqpVPuTUYS4A9jSGT8iTiPzceE093pVIqQi6ppGTYsGBBVOBtdU5uGLTHnQtfmX7LjxoEl9s38eGijo0TWN4hhOrrsX05qMnE8kk+5OgnqSBdPU31ojlMsN1da0E16HrOkrTWb5lOJ+XDQk9ddL1FPa5R7Ot2o9FayI/DSYPbcRvAOwPRH2GwmrRADt5aTWkWAIT3UQSr5vmrBQbZque7vx0B1WNHlCBJ2mTh9STYfdjs9AyvVZgona0wL+cVj/1uhUNCw6bhVSbh2a/FUNZ2R+6Bz6bRmRUcdQExd9WZwMahqnYXu2ixu0l1e5jdn41TquB26/j8o0NHeOrvz+eU6c3cPg4g9W7G3lrg51ddQaWPAcev5UUiw+f4cdq0XBYLSgFXsMADDQCM4Qq08TtddHsa8RmcZDhHNIuRSd4XE0Fbp8bjy/4tEyPOOg1URMzCZEIPQq6L7zwwtBgiNays7O54447YtKw/qzto1cF7Xohviit5EdPfchRk0eGepra/l1hbjpew6SkvA4I1Hm1W3Sm5mW1y72L9AXXVb6pAkqrGthZ18jIzNR2vVmmghWbkq8uck+0fUx+yoyhFA1rwusPH8wUZKrAF3yzadBsWFu9nrj83L6eerytigY3/9lWicOqk5uW2q53NF7l8dre/A3PcFLe4GF3XRNT8jN5/MfzGd5BebXu6slEMom6KYpGT9JAovmbnbVNDEm147R1/BXS2bXSeh2tA9RGr41Pt+8fpDs130ZhrgNNg7nDd2MoAw2d1oEoBDokAiljisLsCjZUFURcb7xumk+fVcBL32wO/Wy36GQ77NS6mzlz5naGZ3hAo2XgduD80jAxzJYByhpkphi4vBaU8mOYJoaytsycGX6Do9AZkVHLyIxsdtYFvoPrPM0cPm4PE3MbAxPmEHwItZZdDZlU15eyfO0XWC2BIFengUNH+yjKyWFT9QjGZVfjNQKBt99QuIzAe1h0Ex0TE3B7rVit6QxNVSgUmoIhaSPD8rErGtx8uKWcnbVN1Hq8LeUdAzdIgScYWtigV0jsxEyDWbxKFkopws71KOj+97//3e41h8NBbm5ul71Eg0HbtI5gL0SgPzVAKUV5gycseI2UDmK36OSm2tnnahncB2GlAoMifcF1lW8auBHwMS0/q11Q/c66nVQ2NjM2J3LOYzLUH46GYZpc//a3vLJqO7VuLyago/HJhgqm5Pmx6Bqm2b6gmIaGRYeCzL1srgmfBCYZ8nMT4YVvS/F3lrxMfHoS2/a6plgtjMkJBNmuZj+PfrE5Zjd/vZlIJtE3RZH0JMUsmr8ZmpbCvkYPo7M6/grp7FppvY5ggBr8jAxSLakTIzKdnDpzDJV1u1oC7rbr0XDYLDT7TWwWDbvF32Gb4nXTPCzdwbz8VIrr9qdXjc1N59CMXQxPb26Zqj34DdBSrlE3sGgmPhXcJgO3z6TereG0W0i1B8aU+FvKJrae+VbXFN8rbOS9zTkoBQvH7mHyUBemaqlfou3PaZ+VX4lFg3LXEEZnp7ZMdOVD12FERhUA+5rSSbM3BTqFNBO/Ui0lCAP98qap0+RPwfAagD3UU13duJsd1ZW8tmYfdR4vq3bVsLmynoZmPxr7yzsG2+I3FR6f0ZISEyx3m7iJmYToaz0KukeNGhXrdgwordM6vIZJrcfbrjc1UP5PhQWvHaWD5KU7AiPVCXxkG0b7wSyRvuA6yzf1GibV7mZynSmhAZmtVTV5qW7yMiLTjDyineSoP9wZwzT5wUPv8/n2ffiNYM42pNt9jMmuJcvhxVQ6bp8VQ+koFfhS1thfLsvb6rFnrPNze1vfuK91dH56DbPV4C09NHgrFvp68plkn0imu6JNMdvdKsXsi9JK/KbCbun47xxWC6Oz0vCbZo9y2du2a2xuOrR0AqiWaxAtcEO8qGgE6Sk2XG4nqXYXPkOFnkbYWg3+s1kCvaleI/xrrdkfCPIMU2Gz6PxgUu9SICNdt+kWOH/6MN6s0PhiR3VglssUF/NGVJFiVVj1wIBOjcB09Brgag6UgG1qqYu9o87BsytHcMT4WiYPa8LrN7DqOn5T4TNN8NEq8Naw6j7OOWACKzZuYeKQRsxW94lKKQwFNouJ0xrIH/f4vOysheqm5rAe9PG5e3D77KF9bihI0QHNAAWGCjz5y0px4zWs1LkDg/ptFo3SmgaWlSzj2z0j2NfYTIXLQ6PXj03XcdgsgfKO1ekUDW0I1SP3m4rmQI5Qv7uehOitqIPuI444Iupe7Eg94YNJ67SO1qWjWlMqUPUB9gevHaWDpFgtZDvtVDc1o2kalgiD+zr6guso37TC5SHHERjUEonR8gkeqVc9KBnqD3fm+rf/xxfb92EYZktNXMUJkyuZPLSJIanelgoCBun2QImtZsNJ4MvJaKmcYOL2WWKenxur+sZ9LXh+BkVKm2o7eKu329FVr2uz36C8wcN5L/yHo6eMismNSzwnkmmtL266ukwxU4rN+xpYs7eWNJsFq9WC3zCpdTeT40xhbG56B+lXipNmFOBq9vUol71tuzT2p9OFbuB0jfMOHM/1P5jNLe+torQ2n4LMQBAbia5p2CwWNlcPDQTaDR72NTXT7DewamCxWMh22Pjl61+FrrOqxuaoj0Fn1+38MUM4MQ+uO3wqDX7FqyvfxKI2Y9X92CwWTDPwWaNrOhp66OmjRdfQdQs1bjsvrhmF0hz4TBcaCr8JqXYL+E38ZiBYDV5/PsPEr2x8sb2CbPtOtNBgy1bHFkiz+QnOIuT2uWjwptLsM/CbgR70IaleUix+QKPR56TW4yfX6cbEREe1BNz7w4QUS+DzvsLlwW+a1DR5cTU3saGiHqUUbp+BqQKlVw2lSLNbeX9zPn5TMWmIC10L3Cy5fX40tJheT0L0B1EH3ccdd9z+3j+vl5dffpk5c+ZQVFSEaZqsX7+eNWvW8NOf/jRuje0vWqd1tC0dFaRpGnnpgTy2YPB68fxJHaaDBIPjWreXYWmBQULRfMF1lG+6q66Rr8uqO9wGi66BUhF71YOSqf5wWxUNbl5etR2f3yS4BcdPrmR6XqBHqL7ZSrp9fwWBFKsfi95Mk8+BwxYItD1+nWZzLKfMHBPT/NxY1Tfua4vnFvJG8f7qRJHSpnRNY1iaI2bb0VFPrVKK7TWNgVlBVSAYcfuMmNy4xGMimdb68qarsxQzpRSrdtfQ0OwjzW4LPA3Ch6lMfIai2u2FalfYoLcgq65z1rxx5GU4WbenhhvfXUWt20tuagq3HjubacNzetQuu0WnICsVr2FS4fJQ6/Fx78cl6IDbb6fclc2IjCpUhDQTlElWaiEGTtaXV9Po8xOYDFLh0zQyLIEedaXg/Q17+HBLeeDJYatj8PKqUjIcNg4aM5RsZ0pYEN7ZdfvB5nL2VWrcMx321H7O2KwaGr02fP6WVBdNx1QGpjLABJ8ZbL+GzaLYWZ+Jz5+CroWnZPgM1fJ5FBjUGKxfbtEsbKoayjc793F4YSBojkTXFS3PDbDqYBjBp3gaumZit/gxlYYWmms28E4+U8emm+iaapmNMjg+KZC+4/X7qPMYNPr87HMZeIzAE9HgFFoagZuE+mYfGvBGyVAyU7L57ug6nDaDvPRMLjnsx6Q7Mjs9TwYSKQMooBtB99VXXx3694033sjNN9/MCSecELbMa6+9xldffRW71vVTrdM62paOgkCwmpu6P60jGLx2lg6iaRqjs1M5bfYYRmaldXuwVtt803s/LsHcUdVhD1hgMpTmdr3qrSVzfvML35ZS0+QNBdzp9v2zwAEYpkaTz0KazWhZQsOm+9CwobBg0RQNzUN4+IwjYtr72NfpErGUl+HkkMJhvF3swmcY7dKm2p7XsdiOjnpqt7dMsa1rGrQ8/Yn1jUssJ5JpLdY3XZUuD69/vT1ib21nnylb9jVQ3+xrSTMIvKYBFk3Hi0Gzz0+tpuE1wlPMgk/WhqSlcOt7q8JuHqqbmvnla191efPQUbuCT0+q3c3kOOx8U1bNVzuqMJViZ10TOoHxFXnpNS0DBgMDE02lUdk4hBp/EX5jHxOHZbJmb21Lz6qOzaJjqsDU7IW56WyvdrGvqZmhLTP6DktLYW+DJzDuQyk2lNczJictdCN04UETO79uLTrfVjSxetduiveuwTBNbLofu65aerOtYAbK45nKaAmEg4Mg0/lk+0g0zYcJVLktbNiXypRhLkwVPD8Ci5tAqk1jT0MO22sD2+/x69DuWyYgUJkp8Du/Cc3+wGBGnwEZdj+aFrgpCX4uptt80LJfTaVj0Qx0jbDUFVA0+124vFZ8BnyxMyvQ827srygTXDyY0geBjo7lW4Zgt+qMzEzl3o/XcfJ0v0wLLwaVHuV0v/fee9x0003tXj/hhBNYunQpt99+e68b1t8Fe56Xb9zLPpeb4OAZTdPITU0JS+toHbxGU34sFr1gXQ2yTLFayE1NYYiz/URIkBz1hztT5/Hi9huhnw8aXd+q3m1AVZMNUiHVZoR6epw2Hy6vhb2uXIZlfTfm2xer2umJ8puFU9hXWcGK3W7Mlu3o6LyOxXZEOk+b/UbY5EaappGfvr/6QTLfuMTypsswFY8VV7Dhi0oUWoc95h3VZ97X1IxN1yKO6XBY9ECKgGmyoaKOjBQbuq4xMjM19DnU25uHSO0qrWqg1uMj17n/XNK1wLYZpmJrdSNo7Wdf3Fw9jLmjR/LNzmqsus4etwe7RQ8PQ1smk9lQUUdFowdlwr5GLykWjV11gRtypzWwLbUeHyNbzu/lG/fw+fbKTq9bpRRlDc3c95/3mDncjdbyBC3b4cdq0XFYLVh0KzoK0zTQlEaz347bb+eznVPRdT9unx+jJUh9e8OwUAUlDYXdasFvGlh0K3sahrCmshDDaEQDvtqVzfT8hlDVktbqmy2kpRhoBIJepUx8RnB3tATqSlHfbMVmgcCp0DIHBBomwYB+v0AAroDApEUury2sQEBHgrcFmlIcO6kc3dzI5vJMUmwWmRZeDBo9CrqtVivr169nxowZYa9v2rQJqzWm8+30W8G0jovnF3Hei/+hZG8d9mBN41Zfcm2D174qPxbNpB7nHDAOXdOStv5wZ3Qg1erle2PrSbUZjM9pwqIpDBX+pVnVZKNWt5LtMLDp4PI6+WLXdA4qLIjL9kU7sC1Zc+UtusaFM/NwZnp5a/3elsHAWrvzGmKzHZHO0wqXJxToKyDHYWs32DdZb1xiedN110fr+XJvI5np6aHxIZGC3kifKSt3VbOzronG5g4qfWjBWXANrMG2KlpSDWJz89C2XXvqmthZ18j0/Kx25xLA+CEZ7Khx4TNMTGVnQ1VB6PPosAl5LRP2BJ7edZTW5/b5afD6AI1A0xWKwGBFpRTNBi01qlVoPItV19lQXs/IrNQOB5fuqG2iwRfo3dZayuO5fSYWTSfV5sc0A/nNgapIVuxWB5WNJnsacmk2Umj2NeM3g6UEA2kc72zM46tdiu+MrMFpM3H70nH5xoDW8l3R8hS1rtnKhn2podS51gyl4/EFjpHfCGxzMDA2VKBsYZPfhmFqLQG3TmATW8YbGQTSTzTVatIchaF01lek88+Nw8LW11HMHUqm0TWOKapgZIYH0KlsbGZ0dqpMCz+AtC5FKOUD2+tRhHz00UdzwQUXcOKJJzJ69GgAdu7cydtvv80PfvCDmDawv8vLcPLmBUdw+/LiUPAKXedj90X5sWh71ZOx/nBnTGWSYS3mwgN2hQYY5Th9pNkN3D490MPd6ivZNHUavVaGpacwJGUcT/7kyLhsX0WDm1W7athW1YDVaiG/ZVKZttU/hqWlJG2ufNDIzFRGZaV2GjzGKue/7XlqmCpQhFjTyHHYApUv2kjWG5dY3XQFa6ZbO3ivSEFv68+UW95bRfGeGlweX8QB8s3+wGBiqwa5aSkUZO9/ghFNzy9Ef/MQbNe9H5eE5gto3Y5g5RFLy83d0ZNHkO6wt/s8uuW9VaG/jZzWF6jEQcvpo1oKZHtbKhsFJmkxsekaFl0PG8+iaxp7G9yMyW4/8NxrmNR5fKAUzX4L9R5vKPjd12RlSKrCiYnf4yXTYUcpRYrFQop9NP/dlUO5K5DqorV8/gZvnGwWDbfPyqfb83D7/ORlOJkwZP/n0thsjalDKrBoPhq9OpuqHBRmN2PR96d5GKbOJ9uz0YCioW4w9/+uzm1B1xR1HhuZjsBkZ0PTMmhsrsTV7MdQCqWg3OUAFBl2I1DnGwsfbJvKh1t9LQONOz286Nr+alAZdh+ThjTS5NNw2vTQ92Fo2UE4LbwYXHoUdF933XXk5eXx9ttv89prr9Hc3MyIESM4+eST+fWvfx3jJvZ/yTp5Rlftaltd4eL5k5LucX0kq8tWkGLZi82q4TcCH/bBgZOpNgNSoappf9qMpkGq3crEoVkcNvkonPbYbmPrgXNNXj91Hh8KH1UuTygqCFb/UECVy8Puusa4TakeC6fPKuDdTeUd9mxB7HL+256n/9qwm82V9eRnODssZ9k24E+W8oxdVROB6G5WelszPcthZ2haSqgUadj6CQyCQwV6JvPSwycuiabnF7p/49P6hqTtQNlgBZmqxmacdivvXbyo3bXRet+2nnAnyGuYgZQoXWupZU3LTa8RFjj6TIWuEzaeJT/Dwd4Gd+h9gjfJAPUeHx6fAUrx/iYnFxyotUr10KhqsmPRTDJTDDx+kzE5GSycfDZ/+ayc8sat7G1w4/WbwUwPDKWwWQJpP8Ftd9psWHWtZYCpm4NH76QwuwEIDHoN5IrrlNakUN9sI8UaqLz05c5MXN7AuZRR6uPggvrAbJU+K//dmcnh42uZlucizW4N1c2udFkAHxZN0eS3omsWNA18yk5Ts8HWmiw8RjZDU+vZ3eAOr63O/s+xwDkANl0PBPDAgaPr0FAYJjR5fVj1/elpQTItvBjIehR022w2LrnkEi655JJYt2dAS8bJM6B9uwzTbDdAqj+UtANwexsoq9qCq9nEokHw4XnrgZNOm4lFMzFaJqWwWXQyHRbys8bFpXelde6r02YNlX9sNgJfmLaWaZchEGzkpKbwxfZ93L68OCmrmEBgMpCu0pNinfMfPE8Xzy3kJ899ElXAn2zlGbsaS9G67Z3pbY95sB3Bc7H1e3kNM1TLYmiaM2LOd2c9v0HdfdLROmhuPVA2GJAF04m2VzdGvDba7ltd02j07Z/JNzATJKRYdTwtAwr1llSKQODXso6WvGrDMCmtdoWePk0alklJeR0NzT6UCvTC+0wVmlxLB5q9toipHobSqfEEBqy6jWH8d88uvti+j8KcdPx+k31NzSjV0iuMIsVqIy3Fgttn4LRaUJpiV20ju+vdHDupnIJMF0ZLBRRN00AFAv2JQzyUVFp5c31eaHuCIXCTz84HW4eG2qQUvLV+SMt+aAo8ebNaqGqykmoLHLdmw47DFth3ptIorc3m7fVDGD/ES4rVgs2i4/WbYb3ZwX1By01NcF0akNpSMzwg8GRhe8vg1iCZFl4MZFEH3S+//DKnnXYaAH//+987XfbHP/5x71ol4q6znr/+WtLOME0e+uQdmn3VmCpQbqu1/QMn/WSm+Knx2LFb9UCPkjY6LvViI+W+js1Jw28q9tQHJpHxm4HeLUurwYhaSy59Mg4GDIomPSmodfm93lYqiGY8QjDgv/W9VUl1Lnen7Z1pWzM9qHUvrK5roRJuHbXD2zLYOKxH2TRBKTJSbEwYErlEYuue345090lHMGj2+MIHyramaRojMp0Rr428DCffG5/Pk//dHAqMNQKzOvqMwDZZdB2bxdJSzSTwd3aLHpb2p1BofkLlMINPn6bmt5S3U+Dx+fErwpKYg4kSb28I5DkXDWlql+pRUpXKii1WUqybyXIG5klIsVlIsej7g1alcPv9GChQikavH48/8O4Z1mYm5jZimITyrDU07BZIt/vRNcXBo2tYuTuDnfUONI1AjrYWaGsgo0YLzcOgofHmhmF8ut3g++MaGZGp0+gdxva6fICwwaqltXnUN1tp8leztryOFIuOw6KjTPCZgbreNh38CjJSbKGygR6/EQqzm/wWAjc/GlZdwyLTwotBJuqg+5ZbbgkF3ZEqlwRpmiZBdxLrquevy9JYSVwZ4vblxVTW1TAyo/UDznDBgZNNXp3S2lTy0rPY1TCcJxYfGZcR85EGzmmaRopVx2nV8Qe+V7FbdSYPzWw3yDYZBwMGRZM2ZSqT1WUr2k0009tKBdEE/MlanrE7NysdaVszPVhqr/VERQDvbtiN2bLOtj36rdvR6PWxr7E5MMOkRSNF15k4LLPDCdFsFp3JeVm4mn29unloe/P/ndG5vLJ6R7uUg+A2BgfNdnxttFSlbgmIHVYLRsuMjlbdgs0CU/OzsFn0lv3lQ0O1lNEL5DBbtED1ptYVOTJSbKwrryfHaWfi0AzW7K3FCoEbG13D4zdDgaxC460NeaTbfRw0uh6nzQhL9bBoCps1MDMkwIhMZ1iaT7Nh4g1E1aTbvBw0uhaH1cDjt2CzGC3jVPRAfXq0lom+jJb9Ekg1ufTgMr4oy+aDbSMIVuE2TROfqWj2h+dR+wyTikaNN0qyyEm1Y7foFOYG5oLYUFUQtuyeelcgeG/1BCLVbsFnaHj8BmgauQ4bBTlpZKbYWL23Frcv8LxR0zS+3JHJnOENoRkrg4dqMEwL//gpawGp1z3YRR10r169OvTv9evXx6UxIv666sWO5QCpvhQMsIpybYDCZwQG+OhoEXoEFab6/+ydd3xkZ3W/n/feqdKot+2r7V5vtw2LC264YAMxzQFDABMINZhiMITgEAIxoTi/OMYxEMCJgYCBAHYMwfZi4jVg3Iu227ur7ateZqRp997398edGWlUR9oZzWh1ns/HLNJc3Xnntvm+5z3newx8pkF/PMm5zfUFE13jpQHYjsZjmpkbsMLnLYj7x0wwUdrUC0cezrRUz04TGNupINfc61wEf6naM+ajxmO4Z3rCstnX0cdg0vVd9hoqY+HoM81xI/rjjeOy1fO44RdPTpoC850/P5fvPP7itCYP403+TSPdyl3j4KBQeE23sHF40exY90Z7OMqjB9pZXleRVYA53F3nUM8AVsp3fHgHTMt26InGiVsOgZTV4HArTFMpwvEkvan3HC7KASxDZ0R3mkjCy28P1I367E4qNQUU3dEE8yuDmTQfrd1nl6ng1StOsrJuIFMMroCaYBKloGfQ6wrcYIKQL/W6UpT7TBK2g8fwcO7iQTbOO0xrbxndg5pnT9TQOTB67SM97FjqmHmUojbopS9uZRWwgrsiEvB63Lx47R6jdJoNKWvH5XUh2gfi9EYTVPg8WJbtTmgAj1lOJFFPTbAnMw43Ip5aaZC28MJpzrT9/fbv38+KFSsAOHHiBA888AArVqzgla98Zd4GJ+SXXCJ/hSiQKhTDUxYea+3Do7y09jaysKIjE7WC9KTCfejXlSUp89q0KajwDxDyx9g07wWeOxzJiz/sSNFowJiFc+awAiINYzYhKuWOn7kQTYRp6zuIYYzOCYZspwKfp3xaudcTCf6p5j3PdLHlqdZ43PjKNTywo5Xd7f1EEpZ7zGxNUrnL+0tSkcPJIvpjjSOXFJj5VWXTnjyMNflXwMGuCJ0Dcbfg0XSjuZYD1UFPVkv6se6N4ZMsv8fMclyBlNDVmu7BON3RBPXlfgIek4WVQTyGgc9TRfdAjK7BxCix3todyazS9MWSo+wIAx4Ty3KwyY245U5SkrbNnrZ+NsyvAuBkOIrWcNWaDtY0RNBawbDum1orQr4kqgzCCS9lXhutVarJjYPGwVQaj9dhvtckZscIeGzCcYt1jf3sag9y/96GTJfK9HHXkGqCo4k5mhdO9FHuM7MKWJVSOI6DYZqsbXTHu+tkL0nH3ZvHUHgNg67BBM01IQYTSbcjpfLjNYbscvd0VeMxWjMNjjRuqom0hT/9GG4fOBlzxV5wWqL7pz/9KV/+8pd55plniEQivOUtb8Hv99Pf388NN9zA29/+9nyPU8gDuUT+ClEglW/GSllQOsyFSy32dpbR0uZnRW0iYwkGbnS1vixBmdciZnkxDS8B06C+PIhSnLI/7HiRu3QnvaXV5VnL5Y2hQGZJeWRzlzSl3PEzFw50PJ85P+ORdir4+c6KvOZet4ejPHGog+eOdaMMRZXfm5pMZotIR2tCfs+sLBy+9dG9WFpTE/SRSHnhKWNY98XewUyB2lQj+lNJgZnq5GG8yX+6eNJrKAYdTcA0MnKzP5bk0LCW9MPvjbTov2/HEY71DVId8NIbS2aitMO7TaYLlZsq/HQOxFhUVc416xfz9rOXcecf9/HIS22U+UY/24ZPklFunrjr7U0mb9owwHZG/eko0rWNKlXEOZBMZs6V5TjEkgOsqR/AUAZaudHkdABhIKGoDkJlwCLodVJiGzyGk2q84zqPa8cV4V7DYFmtSWuPSX8syZmNAwDcv7cxM5604HZFddrD2xXDWpO6tty8eNMwWFDuxWsaHOgKE0lYGcuSpK2xHJvOgTiLqsoo83nxmq7zSXbXUUVLx1CDI79pcenqNWxcdA5B39g1BIJwujAt0X3XXXfxjW98A4Bf/epXBINB7r//fvbt28dNN90kortEySXyN1aB1Eiv3HkVwaKKwbFSFjyGScKKsaSql32dIV7srKC5JoKZ6rpmKBu/xyZmeYnbQfwe9+/Sy6an6g87XtpOupPewe4Iy+uG9uv3mFQHfXQNxqkL+sZs7lLKHT9zIWnHJhTc4H7R9w5GeGT/YJ66NDp86aEWvv/UfjoiUaJJd+m9P5bkZDhKU0WQ5mERU49hcLJ/kD8d6iyZYstcGO7TrYGAZ+SxcwvU4paN32NOeXWqkDanY03+011GQbmpCmgG40nK/F4M3KlzbyxJwnadMsZqQ3+sb5AjvQO0Ou7kIH1MjvUNorUm4PWAUpim6yK0qMqD5TiE40kaQsEJ7RzTk2RHawwHt9ttyttbO5p4yvHEqyA5sZMjQEYsozWGYWSdq01NfQQ8CqUMtwBUKbR2qE2t0ik0pgLTMxRXV7i50DpVzqlIFyXaWHaUDfPn8/zxHvpjSVbVDVLuS2asBNPFmGkXFoU7yRhMWmSaT6bGm0wVpbZ2R+iIxDPWi5nPpDWxpJ1xJDENRZnPM2buf9z2s7NjIZetns/W5aV1fwlCoZhW+ObEiROcd955APz+97/n6quvxuv1sm7dOk6cOJG3wR07doz3ve99bN26lUsuuYSvfe1rOM7YoYS7776bK6+8krPOOovrrruOHTt25G0cpwvjOR4MJ10gZTkOWmtauyPsbuujM+JGijoiMY73D/Ktx/Zhj3MuCslYKQsJyyFh2SRsB9uBZTURHmmt4TtPLeSZ45Xs7iijL+7lRL+X3pifWNIhZtlU+L2ZinkYirpOlfZwlIf2neBEOEZrd4QjfYNuIVSK5XUVmVzV9PF3tGZZbYizF9exrC6U9XtDqZLv+JkLXjMwYVtocL+kXzgxMCofdiTpSO1k3LKthbueeInuwUSqcC5lFYcbiTsZjnKoO5LZ51mLajOtw8diuOCfSdrDUW7bvpt/ePB5btu+e9T7D/fp9qR8p0eitaY94lqvTXd1Kh3FvvmKjdxw4dq89BUYa/LfHo4RTVoMJq2U0whoBQOJJNGU+HMch/ZIbFQbetdqz/WwtlNC2HIc4qn7LWk7WI4r7EeuKg0/v9dtac509hxJepIcsx0srfEaxpDaZKhsWytXxFb6k7xqeRevW9POq5Z3EfK5Ex5FKoc6lXtPysXDsm32tvcTT9qU+Wx8HhOP4a4uaK1TgttK5T8rUmYsQDpKnR7F6MmXrS20dqgvD1DmM/EYmlcs6sdIR+hTn2P409zA/b3XNPCaruViuc+DoaBjIE5bOApZSSpD+MyhCZ+hFC9fUs9lq+dnVkvg9HrOCaVPKWnJaUW6y8rKiEQi+Hw+nnjiCd71rncBEIlEMM2x8zenw0c+8hHWrVvHtm3b6Orq4v3vfz/19fW8+93vztru4Ycf5vbbb+c73/kOa9as4e677+YDH/gADz74IGVlZXkbz0yS7/zS9nCUSCzB4Z6BTGHMWP67wwuk/vPJA3QNxjGVynQArA36WFITKloEcHjKwvAGGjHLThkWuFGyzfN6+d3Ben57oA4NvG5NO2c02GjbXSr3GG5hVGt3JGPRNx1/WNtxuP7Hf2DHiR7SxU4a6BqIUR3wZfJQF1eXc9mqeWN20iu1pkn5YnnDJo5270aP4yQDbnSuK7YQQ/VPuK9cuzQ+tO8E4fhQzq0/tYKQTE2CbAe6BuMsqi7nstXzU63DZ6bYMhfbxFx9xYcL18aQn+5oYpRhjxpmDTfdVKWpPIfaw1G+/dg+/nioAzSc39zAX527etT26cl/0JNIWdIlCXkSdA5UMJD0pqLAKcGnSEW+3WjzFavnURnw8Te/eoZ7dxzNrFQlbDdi7TXcSHm6u2T6e1Wl9tPg94zqAmsYin9/bB9/e8WmCXPZG8p9booKrv+/K6B1xi3GSEV6X7emg1V1g6l8ZQDN5vlh9naW8etUPrV2wOM1qC/3MpC0iSYsvFqzpDaE1j4i8TCZ1Dhsgl638DJt9Gc5isGkojpgZ8Sygyvktc4eu6FMoskIjaEKOgdibqTf5wpiPewzDCfhaKoDSS5YGsbvsRmIGzx1vIqoclcHlFLue2VGmbb6MzANIzPhW1hVRnXQx0cvXHvaPueE0qeUtOS0RPd5553HRz/6UUzTpKKigrPPPhvLsrjjjjvYsCE/s9aWlhb27NnDXXfdRUVFBRUVFVx//fX853/+56gDdc899/DGN76RTZtcAfje976Xu+++m9/97ne85jWvyct4Zop8N/MYuT+AjkiMroE41Smf2HQKwPACqfefu5qH9p3AMBS27eAxU4UwKRFTLLu14SkLh3oG6BlMDOVIQuZLKeC10Qwdv8GkicJdyi3zekgHtHoGEwA014am5Q97y7YWdp/sS8ltUu/uDqQnmoBUHqqhFA5qTOFWqk2TTpWgr4KmqmWZVKCRpJ0KKnqqcHRfXro0nuiPZlnmKeU2HkqLMZ2KUl6+ej6fu2JjVuvw8TjVwuGp2Cbm6pE/fNXKa5pUB3wZX+k0OuUGMpVUpfTEIG5FeeSlbrbtL2Mw6cvcRz97vpWKgJetS+qpDvq5bkszdeV+vvTQC9z91AH3fkolOv/pUAd3Pbmfd5yzgs9dPvTceuvmJRxs305TeS9KOTgayjxJzqjvY19XOf+7rwFwo6sGkM68ilk2D+47wY6TfRzrG6QjEqMzNbk1U24afo8JqRb2mlTb90wKmrvP1hHWihr43hP7cYBPX+rauQ23UUw6Dn7TZGFVkMpUvjgoPIY7ObAdTbkvybmL+tg4r5+aYJK+uAfbyVyFGEqzLpVP/au9buMar2lgKvf56veY1Kaerwe6K9gyvw2P4WA7CqUcsuWtO+Zwwk+FP45XuWkmtqNIojGVzkz/He02AopZMRpCFVQHfdiOQ9Jyu12mGo9moZTmtas7WFM/mOqs6e5rfVM/+zrLuH9vg/t3Km3/nb4OjcwkNz3hGz7Zm+w5VypdY4XTi1LTktMS3TfffDO33nor4XCYO+64A6UU0WiUhx9+mH/7t3/Ly8B27tzJwoULqaqqyvxu3bp1HDx4kEgkQigUytr26quvzvxsGAZr166lpaVlSgcqHo/z1FNPARCLxWhtbSUajRIIzJxR//cef5EnD3eNucx5X+s+Try4i7/cumra+6sHrP4o4YRFl+NgnfDRVBHEYyg2L6zhypo6nnrqKe55rpXY0TZqhw0j2Q/DpYej4Wv39PGWzc3T+7A5MvxcHA8foq3vEJYDB7vDmW0SlkPCTnc703R2lmO0D2IaBo6jearPYmG0Hb/HyEopATgBRGsr8JkGlUvX8tSJp3IaV89gnF//33MQjqEH41nLzZltlKI6HMJUin5fP089lZj+gSgBpnpfaF1FXzt0R45kCU5DGdSGFtCQqOJMuvnxsZcmtKgzlOLMDaHM/TkWe1peJHHsGMSTo4SEAfhT/7/M7+X4i5qnahOED7cSPtzGOFkFgHudn8q5e7HtSTr6j2CMMVne6xxkd+WLrGp6WeZ60hpGN2d3+d/jB3hFIMKZwH8d208sGoVAkHpDZe7r4Qey0q5gXVVd5r4eD601L7U/RXfkOI52OBmO0R9LsByD7miI/d3zORGOpZrOaHY+5z43fvwbV7Qd7RlgIGGNyuHvBv792H7aXhp6br3Y9iTlPfs4fsJtv560HZKWg4OmFjg/cIzHj9aizCF7u7jtEAdWN1QS7TeI90dRqfPcA1m9x/2Aj1SE3HGVtTdlAzjQb7G6Lswq0yFuG+zrqiCaMHHK/Nz32+OceHEX1798JQfibWzf30YyFXAoC/p4dn+EuO3gN43MtaTQbFnQzeLKQTyDNrrNojv1Wsw26I9lf8XWaEVNf5ik7SPg99CTTNntaU2izEe96qHZ20f7gQR+00Gj8ZjQr91VmrScjtsG4biPiN+izJdMOY+AoxU+041cO6hU10pN0vHyvNVBhd+LBl7aqwj0HSI+rIGYkTqE5y3toS4cpSM8fOTuea1Fc1HZMbYfqsFINcdJ2A4e5fqVkz4NWmMHfayqWcjhvTs5PO6V506o/+OJl3j2WDe2Q+az/Pg3sGVhLde/fOWkE+PhTPUZ5ff7J90GIJm0ObS3PedxjMffnZm9j398ZNkp7/N0YKLn01jket6KoSUnYlqiu7Kyki984QtZv6uoqOCBBx7Iy6AAent7qayszPpd+qD19PRkHaje3t6sA5retqenZ0rvefLkSd75zndOc8Qzw37gx3nalwGEU/8B7JnGvl8Cvpun8eSbkf4r9+b0V3fkfRwHU/+W8rGaDbzlm/nZTxj3Op/KtV5K524qx+FA6r98PTPAlV/DnxvDfz8Wg0z9ePtG/Jz+em3N8e/VGPsAiAJjVW30pf4b7/naO2IcaTTwTOq/qWDgHpfh4+wHHprifk4FD2MLgOdS/03EcI+RsY4zuJ9nqud9JHtP8e8n4+6772bp0qU5bdvfPchtn/pl3scgfi0uV9z//3LedirnrRhaciKm7YP12GOP8ZnPfCYjUh3H4de//nXeBgZDy1b53lYQBEEQBEE4/SklLTmtSPevf/1rbrrpJi644AKeffZZwI0S/93f/R0DAwNce+21pzyw2tpaent7s37X29vrdgirrc36fU1NzZjbrlqVexoGwLx587jvvvsAN9XkxIkTzJ8/P+dljFPlP58+yPPH+ybdbvOCKt559uRLUqeyv95ogi/+dtfES/7A5y47k+rgeLGO6fOfT7fy9NFuQNM9mCCetPB7PdSW+VDAuoZ2KgNhVCrnMOnYJCzN4b4gfzxUjZPyuHJtrMA0FT5DURX0sbDSYGFFDx7TwbJNjvZXs3XJfN60YfEko8rmv1uO8GhrJwZuh8WT4RgDCWuo6U3q3zMbK7nlqk0TpjDMFgp9X/RGE/z2pTYGEhYhv5dXrWikasT1ld4mkrAI+Ty8amVT5hr8z6db+d89x8dMdXC0JuTzcNUZC3jX2c3Dfg/ff6aVluO9WGi3iykaD4oNC6p5x1nN0z53B7uepXtwcken2rL5PHO8PnM9jYeD5pXN9bxpw+LMuQjW1PH7w72jjln6HhqrKPDFjn7itkPAa7JpXg8rayOkY9W21hgKgl736yHpOFi2zf7uCp4/WZPZR8jnQQPd0QSW7eA1jVHWl8O3ffWaebyyuXfU8bBsh9aeARK2xrLdlIrW3iC/P1STtZ1HGVQHvSyqcguaToSjhOPJrNSD9D3npFq3z68I4jHjrG84mGqQpVNt0d1/DUO5jcC04pmTzRzrd7Bth/mVZaysO0F9sB9NyhnFGXIfcl1EDHxmOi9aU+GzKPPaoIysFJ+oZRJJePCYBl6l2NdVxpNHqzAMI+V9rTLnYCjHeiiXGlK+3KbNYNIknMj+2k47ouhU7rihFEGvW8NiOSa98XIs28OxcA1J28vxcBQTUIYa1ejnrAUdLKmKZd53JAq3EU9bpJznTjZiAA0VgWndM73RBP+8fS/7OsN4TLfjqHcMI4bh13wuTOUZNZVnWGVtGR/92utz3j5XJL1kch5+3yVZP0/lvBVDS07EtET3N7/5Tb72ta9x1VVXsXHjRgAWLFjAbbfdxpe+9KW8iO7169dz4sQJuru7MwempaWFlStXUl5ePmrbnTt38oY3vAEA27bZtWsXb37zm6f0nn6/n3POOQeAwcFBdu/ezdq1a2fMAeVpp46XPIcnLShbt2EJF1wweeHdqe7vWV03aVe6114+5F6Sr0KY9nCUAy2D9FWV0RtL4PjAsW1ipklYQXXAR2vZGvxmjFW1XfhMi0TSYNveAAO2F/8i180ArWkIBegf9sWyqKkKv8eka9j7lVcr/uYvLpjyWFdvitLyg0cz36+VMKr9dFNFkJ9df9FpUxA0E/fFay8f+/fZRcG1GF7XW7jlJYuLVtTx2cs2cO5552V8urtTRbKkhFh9mZ93nLOMz12+cVQx8oWvvGBCd4XpXts1x+Bwl29Cv3KtNUvq1nFl5dlZ19NYGGroWh1+Lt404ly0h6McbBmkZlnjqH1E4kkGnG5XfHo9NC0P0FQ3kHk9bVMX8nsxDUUs6RYmhqvLUXq+O2YgkLbe6x1E2w6OoVDe0aLJchyiHpOjwToiNWU0L/bgH7Fd8lg34Xgy5TaiOX6iEj1Yl2Vjh4JOpfCFgiyvDbEKMu5FTupet2yHvliS2oDXLY4G1tQdYXnNIhwHIgkL01AYqTzvoWejxm6q4viL1QRNk/pakw1LIijlustEEha2rbOceGqDScIJL5btinhTOSysTGTOdXrLaNKgJ+rHYyj8Hg9HQ+vZ2OjBsh12t7lBkfQ5cK0Rs98n835lSaod6Il5SAty2zHY11XGr/Y2UOm3eNnCfsp9Nj5PkP7EYuK2H7zu87CDGPGERY+K01AeYHF1OeG23sxAHQ2e+UGa5vUBRmYMatj/Avg8CiPSxB6zniU15VQHfaysr2BhVVlOjiTD7+NWXyPR2hq01oSVGlXcn/nsK+ZxwQUbJ9xvmqk8o1paWnLaJ4DXa7J0zej76VSxdzXnfZ+nGxdccEHWz1M5b8XQkhMxLdF9+PBhrrjiCoCsm+Pcc8/l6NGjeRnYmWeeyYYNG7j11lv5m7/5G9ra2rjrrrv4y7/8SwBe/epX86UvfYlzzjmH6667jk984hO89rWvZc2aNXz3u9/F5/Nx8cUX52UsM8V1W5r5n51HJvzSnYr116nuL9eudPl2XPnRs60c7AzTmxLLQx4AZFxBtNacMa+KnR2BzKTA64ngJOJuG2jToCboo7k2RGt3xG1CU+YfZZM40tkhF3E1fBufx6BnIJ7pYje8/fTp0OCmlMjV2ePzV27ig+et5t8f28cfWjtQSnHe0gbed96qCQXBWO4Kp3pt52qb6NoHBnNqv57L9TRR99mDPZHMMyFhO0Qtk6HIKplIccyyKfd5UvXBDjFr6N4Z7nfdEYmRtO1RQklrTdx2SKbGEY5b3LvLy0XN/dQEvRlxNRC3iFs2OhUxdhzFn45UMraLLrRHYphK0Vwbork2RNxyuyC+fv1iFlSVcdnqeTy07yS90ThPHO7EaySxh0WQbceN8oLbjj0dHTdVEr9pUlfuZ3HlURK2OyYrNQmB4aag7v9W+hM42sBQGkcroknDtfdTBmbqcCgMPIaiwm9y7vJNJM35PPJSG6bHpCLgpT0SIxxXqUJjI7M6N5KuQQ8720PELZOg1yaaNHn8aGWmyU1f3MvDB+swFZT7fZzZ5MVrwqFhbi1xyyZhOXREYkQSFlqD14izdXEfPsMmaTvuVaCAVHv54biFr/CbfQEiiTjzKoN0D8Z5+miCkN9DbdnkEcjh97HHY6JJZq6d7kG3hDjdeRSK3wFZmN2Umpacluiuqamhq6uLxsbsWd/BgwdHzRxOhX/913/l5ptv5vzzzycUCvHWt76Vt73tbZn3GhwcBODCCy/kE5/4BB/72Mfo6upiw4YNfPvb355R15F80FiRvy/dfOwv1650uYqhXDneN0hfPHvZczgK6ItbrG+qpiLgzUwKltaUo7WmL5akOuBlaa3bdGZZXYjmuhBKp5fOR08eRoory3Ebqdz+6G7WNFbxnT8/l8aKwCgBZmtNx0AcBhIsqgq6binjtMsWps94rcPTjLSwbKwI8rd58JA/1Ws7V9vEtF/3VNqvT8RE3WctW2dFeJ89UcO6xv5MR0FIdSgcZgUXt9zt3L+AmoA3k0pSG/QRT9oZkZkmbjskUmkntWV+fKZB0gnQFq5G6aG1prZwLNNMylCwu6uMcGK0yLI1bloEZHVwNA3FNesXMT+1MvbQvpNct6WZbz22j0jcwtY+FK6w9hgGSUeTSI1t6Dg4HOpNUFfu42R/lMUVERrKdCbyPBLDAI9yqPDbWI4xbEql0ToVhdba7Sqpffg9Hl6xbBOvWHEFvzvQwuGeCN3RBLGkK+wfO1LJ+qYwSo3fcMx2DLa31mRE9phosDREkxYnw1EcR2esJBVgp6wU45ZDworzZ2d0srJ+AGNYOkt10MJUmp6YP+OWMrR7h70d5UQSXmqGddLN9X4YeR83hQJ0DcQy0XZDZXdShel7zAtCmlLSktP26f7sZz/Lpz/9acDNedmxYwdf+cpXuOSSSyb569yZN28e//7v/z7ma3v37s36+W1ve1vmIM5m8vWlm8/9TeSv2h6O8qfWw6xrcJtcJGwvrb2N7rIm0/PzfqkzjDNJwxLHcdjfFeHON2/FAP7Q2gFK8Z6tK7lwRSP/sn0vvdEENWV+vnjVJs6cVzPh5OGLDz7Ptn0nMA2Dwz0DWT6+fzjYzgXf+A3NtSGSlpO1LG0qxdKaEIMJi3K/l1csbZDGDwVgoshtmnw1sUkzVaE/HhsXXwowyqfbUAYLalZlXof8tV+fqKW5x3SfASolxSIJL3s7y1hTH0kJRjfirLUiZtkETMWxcBX9cQ+moahJTWjTLKwqY2ltOYd7BukZjKO1KziTtoPPNGiqCGZtv6OjGYBBqwu/OZTOYjmKvZ3l/Gpvw7ify9Ya5bh/0xaOsbSmHI9p8PTRbp443JV5vv38hUMc7RtkaXU5rb2NLK7sRClNwGtixS23KyQ64zXtaMXBnkbCiSh9sQSDCTf6Pzri7Ir3mkCCgNe1HRzysk5FwhUMJhW2NumN+nixuxpbLSNhzOMf/+9huiIxjvVFsVJ2hgqIJr3s6ypjbcMAYzVnTU9GJhTcuJFoj4Kk5dA5EMNQKuOlnV51IDXaq9d0sKZhwB21UqnUHkVvzEtDuUVjyKZ70IOVSvmxNezpKGfb/iYaQ76scwq53Q8j72OfaYzymE831llcXT7rVgvf84t1xR6CMAalpCWnJbo//elP86EPfYjXve51gJtWorXmoosuyghxYXrk60u3UPsbjqMd/vvZezl/8SEM5ZCOlCyu7KQtUs2OjmY0aspiaEV9iP97aeIqHAW0R6K87QePZkWd73pyP997cj+Lq8owDYPuwTg3/OLJTCrAWGMYLq5auyNZkSFwhXVXJEb3QJzaMn/W0meaMp+HhOXw/nNXzZoviNnERJHbNKfaxGYk+RL6hjLYvOSyrI6UPk+QZfWbCPrGNgwba6I7MvXp9Wc0jfueE6WWLasJ0Rvtdu3qTIO4ZWcanqyuG8BQbnvvcp+J1oqOaB3LG1/G+bFuBuKW25wWMpP3y9e4k/eugXiqI2Unh7rCxCybBVXlo4orNYqnTy6ltSvI2Qv6MM0kkbjBk0er6ItP/pVkKIWhFKsbKtiwoIbHWjtGrUQc64/SNRAH7aYqtEWqmV/RhZ3q1mikmulo7TbfOdJfRZmvguP9YRSKl3oaWN/Uz7AG74CbVlPhV5R5bTSKkxEf1QGLoNfJlGeCJuCFRw9WcO+eeZiGycb5mruPH6A7Mylx0JApmrW1GnYOBjGNoeep7RjsTuVtT4bWoEwDrwHRhI3PVHhNk5jlkBzW9jrkS7KmfjAz+TBSRaWO40bno1YQQzl0x+rRWtM5qPlDawVdUcWG+TWU+8Y+T5PdD2Pdx0trQ9AdoTeWzKT6pANEslo497BvfUexh1BQpu3T/YMf/IA9e/Zw4MABAoEAy5YtY9myZUQikXyPcU6S7y6Fheh6+MKRh1H6aFakx42aaOZXuMvHLR3LpiyGFlaVUxXw0hMdW2g5WqMUHO0dxOsxsW0H0zRIWDb9qQd3+ss2l1SAtLiyHE1vLLurX5pkqoiqN5YkkYrgjSTfkVZhiIkit2nynfuZb6Ef9FWwbuEFk284gvHyyu9tOcSacs1X15wx6m8mSi0L+b2U+zzEkjYo3Igril/va2S7L8k5C/qoDmrqyitp7W0k6QT44V+cweevDE44eW+sCPK51P31Dw8+zyMvtY37mdoiMbqiJg+8VEPA62EwYWXSWSZCQaZ9+cYFNTx5pGvMlQjbdjCHpSqko+tVgS7SEWzXzUSxt7OCbfvriVm9aK0JeD1YOkh/vI7aYCdJO/3O7vh8ZgKlNAMJt6DR0QaxpMbngaRlYDmKcNykL+ED3C6N7ZE4vanrKWY72I4r9tPpPBo32n7/3kZCviRbF/WPmbc96fFRbvqQ1zSoL/eRsB2CHpOYZeM1DSzbXVV4+aL+YYGSTBPRTOTdve4NLMfD3i7XNcQ0w8yrUOMKbpj8fhjrPla4z+qE7dAWiWFZNlsW1vHNa7fKaqFw2jFl0f0f//Ef/OIXvyAQCPDmN785y6nkmWee4VOf+hS//e1v8zpIofSIJsK09R3EY3jQWKOEqsagMdSDv3sBUcs3JTF03ZZm7ttxGNXtOhNkvphSUZAKn4euaIK+WBJIZiyzBpNWyiHAHDMvcLylz7S4aotktxDP/jxkcl7bIjEWV42uis93pFUYIt9Fxrkwk0J/ogLe8fLKbUfz+MkBvvrIHr74mrNH7XOi1LIbXrmWRw+08fjhTjeVKxXlDMc9PHV8HhvnV9MZM1KfcWgymevkfbJjl7DsjM2ggduifXh3xLEwVLpw340nv3C8Z9yVCDPdzXJYqkJLxzLaw1UsqTyJz+Pa7z17ohrbcfM1dSofOm7Z2LbD7s5lLAglmV/Rh5kKLLit09OCGxZUuIV/mVUxr8Ng0sTWBgHTxjTc1I2+WHKo7bwm0yE44DEyqThpIgkvvz1QN+kxHvWZU8cnLeP9HhOfx8BjuMI/bjupiQautWFWW3l3IuI3DQIpZxmNwmdamWtm7bwqBhP2hGOY7H6Y6D72mQaLq8owlOLON4vgFk5PpiS6f/CDH3Dbbbdx1VVXkUgk+Id/+Ae8Xi9/9md/xh133ME3v/lNLrvsskKNVSghDnQ8j6MdGkMBOgdiY25jKE1zdTsvdi/JSQwNFx4Br4fGUID5lUHawlGisTjBgJ95FUF2tfWlclKHSDjucm3S1oCdii7FMk4iMH4kOi0Q7HTlfgpHp6wH0ViO66ercKNoYyFV9oUj30XGuTATQn8yd5T3bl01cV65UvzxYMeYk8lcUss+/LPH+Onzh7EcjddQNNeUEwpk+6JPZzI52bGLuuFjvCnx6feYWI6bCz7yT9KR17Srh9ZQHfQRs5xxRX2mQA9FwnI40jeIbTv0xxV7O+ozE+gyr0l60UopcGxNPFV82BdN8qRdR9BbyZb5vQS9NrajWFE7wMraQTymTuU7D50bhUOl36HMYxFJmFQHLKJJw/W3Zuh9dGp1wdYQSH32CS+0STBUasepY+o1DfpiCdykdddL3HJ0xhEmmsx2rDGU+zd+r0l9eYDGUID2SJSAt4bljUu47qxmtIa3TWJpOdn9UIz7WBBKiSmJ7p/+9Kd8/etf51WvehXgVnp+5zvf4cc//jEHDx7kK1/5Cq997WsLMlChtEjaMZRS+Dyut2rPYGKUvRQoPEaSC1c0TvgQHUt42I5D+0AMBcyvCBD3aAJlQdJfEunGHRnSIjy1XO4z3UjgcMYTD2mBkImO4Ua7LEdnll0d7X5RRi2buhFdH/xmnObqdvymxSsW+YgmFmXcKKZDvvzOZyMTffZ8FxlPxkwIhMncUf50qHPSvPKk1hOmNU0UnV7dWM26ef15j+ZPdux8poHXNDJ2iwq3LmIgYaV8qodwI9xDqRgNoSCLq8uoDvroHnQtQtOpCelUs6ZQgKqAjxP9g8RSK15uwaLtFmNq8HmG3l9rnfW+rje5K1Etx8OjrfVcsaqNtQ0DBLwWfq/7vPEosLWD5YDHcKPNKDANTU0wwbu2HGF/d4inji+mJ+quynkNRdJOa+R0c53cGX6mjFQgwPUeV3gNg4RtE03aeJSivtxPx0CchO1k3kMBjx+tZNP8cCZ/P3OcgcZQAJ/HYHF1iAvXXJX1LMvH/TDT97EglBJTEt1Hjx7lla98Zebnyy+/nJtuuomLLrqI+++/n4aGyQs9hNMDrxnIpHssrXGjyb3RRFYfNYVmWW0NN1wy8UN0LOFhGgbNNSEGE0nKfR7WV5azcvEC4o7mRH/UjWINFwpqWBlTaonYHCGOxxMPaYHw613H6BqIEUvaJG03bzz9Ze/3uF8yaasxUp9vfUMrTaFewKGmzE93ZB/b975EU9UyNi6+FEPl7k+eb7/z2USun71QRcHjUUiBkIs7yt72PuZVBEf5yw/nVNKaChnNn+jYbVxQw64TPfTFh1LTDFKCNGXt6QpUMAwDx9HYQNBr4vcYOBq++OpN3PDLJznYFclyG9JA10As9bNyo+la46SKukltM7zdc9x2Mu8J7vuHfBYvX9xH0GOzoiZKud/GdkxCPteT21TuG5qp58Twx42jFSGfpi+uObMxQmOog/96oRq0+2zzmpqE5WABlmONaUs4knTha8BrEEkVtBoKAh5P5r1jlp2pPwkGvKxoqMSyezk5MJQGo3FTWF5MuaVo7abNmMoNoPg8xigry1zOaa73QzHuY0EoFaYkui3LwucbWnoMBoP4fD6+9a1v5X1gQmkzvOmHSjWqSFgO7ZEYluPgMVyrsMvOvHpCoTiZ8CjzeUlaDm9cWc35Z63k679/kaaKQKaJQhqfaWClxXDqS7AxlO2tmUsjoJOPR+mPJVPRxiGfYn8qCh7wmIRjSWKWzcvmH2ZeqAuUQXXQn2n2odEc73kRgM1Lck+3yrff+WxiKp+9EEXB4zEVgTDcncRrBlINb8Zf8cjFHUUpRVs4xpKa8fsfnEpaUyGj+RMdu3SqgiuYU64VkJpc2BlbQNfb2xXBHkNhGgYdkRiO1vx3y2EcrekajGOqYekbQNJ2GEi4aWa1ZX56BhPEbTtrm4StAct9D9u1cvSZbpP4K1a2Z5xcTKWZX+lG1GOWiaM1lq3AJJXr7QrvtGy2tcJyFKaRajmvFbXBbhrLKmkbcP1K/KaB5TgptxAy4nUs6T386tAq1dAHCKaOzUAimallSeeG+0yDMxoqXReagBdfLEHScY+xmcr7/vW+RqCDVXUDeAzweUyWVJehUKOsLHM5p1MVzDN5HwtCqTAt95LhTNTeWDh9Gavph89jsKjaLTB0IyUrJk2zyEV4JLXmgUP9nH+Wm3/tNY2spWUgU4yVdDTa0QT93qzoYK6NgLTW/Ov2PQxadiZKZhhGypvX9SeOJS2WVBusaRzAa5TRWBEY5WZiGCZtfQeJJsI5pZrkyxN6NjIbPvtEAsHRDi8ceXiUD/fR7t0Trnjk4o4yryLI8b7BCbfxKnVKeeWFXu4f79hdtKKJpO2wQJNJDfGYBo2hAEnboScapzMSJ+nYlPu8qcm0oiboY0lNiF/vOkZ7JEpdmT+r4DpmDa1GWamVMSsl4AMeg6jlniPbGcp19hoGPo+JoeCylSc5s2GQuAWgqAhYKOWuowU8Fk7Ky9yyFbZSeE0nlacNljbQWqWyvN1W6m7et82Gee2stgwCHpvBpMETR6uIWT4sxy16NJQiYVkkNSSsoQBCOiCvwA0saLc5T1K7hZiOViRsN3LuNQw8pqK+PJB5/tmOJuj1YKRS5jyGgc9jkLAcHnypiWdPaK5cFaWxsozmhuYJrSwnO6fjMVdS5r77hp1ZP5/Ovt2nu7VfoThl0S3MHUY+ON+6+Vwgt6Yf45GrLdtA0v0SSi+HZ6W0pNJcXOHrgKFYVe9+aUxZPCjFpoW1WTmiaSGQFtZBr4eXL+ylORXZHg9HOxzoeD4nm7hiNH8pFWb7Z3/hyMOZyWf6eshlxSMXdxSPoVjTVMlA3Bo7Eq015y1rOCUBM5PL/cOfIRWpZlLPHO1mYWUwS+xfsWY+7926ird8fzsnw2Pfh13RBL3RJGubqphfGaQ9EqNrIJ5Jw7C1zrRxN5Ryo8CpXPKk7RZGeww36uv3mhhAWcq/2mt6SNoWAJ60JWoqEq0ybtyuw4fW7rl2Uuks6WvA0n68hmtFWhO0ePnCLnpiXjyGW7y5eX6Eo/1V7O9Zgdc00aRbtifxKouY5WQa5XgNhv6/x8j4q8dtB7/HJGi4+ep+r0ltMLtxjZlKr/GmvMlBY6KYVxFgQVUZPtNgX7fmzIVLOHNBfoX0XE6ZE4SxmJLoTiaT3HjjjZP+7tZbbz31kQklw2QPzo9f+HIOdb2QU9OPkeRqy1aesrEavhzeXBsibtm0R2LYjrv8XFvm48IV81hQVTYt8ZAeT9q+arzxBLxu7mR7JJpJp0kXIKVRSpG0x3Z2GUkxmr+UCrP5s6etMy1H0d4/OOpamGjFI9d86u/++bl85/GXRkWiTWDrvHJuumi0T/d0KORy/3jPENNQnNFQybF+N62rdlgH2du278ZUKus+jFs2R3oHsB1NXzSJZdsZl6LGUICugTgBr0nMdtCp3Gad0swqJbyDHgNSwjvdlzOetAn6PJy/JEyZ170W0ytnlk6nmg2lsNjaTR8ZUTaCxzCwHYe47UVrg4AXyr0xvKbFQMKDz/TgMw0qA17C8SRn1PdT7m3lwf3zsB33eKysC9ETS5KwbPqiCWrL/PTFkkSTFt5Uig24k38H18nFLaZMUuH3ML8yyNHUMTINRZXfw7E+J/V53TQSRzv0RhMAblfPEal3EwnqqQjpuZwyJwhjMSXRffbZZ9Pe3j7p74TTi9wenFNv+gG5CQ+vUly5tDLz8/DlcK9psLi6PBMhu3BF4ylFT3IZj6kUfTHoG+zJfGlroHMgRnXQN5TbrTVeMzD+joZRjOYv06U7muTf/vgigw55WSqeTZ99JPvbn+Ngdz+90WRWEfHwa0GjOdDxPA2VZ48SMrnkU8+vKh8zEv36M5roOHJwVMFwoTiVFIGxniFKKQ52RXj6aBc1AR/L6iqyOsgOvybils2ejn6iCcuNTHtM12HIdugaiLOoqoz2SCxr1cuy3UjxyMvKStn0eU2DujI/lu0waNmc0VjFvIoe0lnUAa9JMm7RHzMo96ms3OqY5aaclXltFAYoO+VyZOFok8Gke60aysFnWoDCcvz4PW5tSDRpozUkbJvFVSfYunCQ/riXZ45X0zXgozrow2O4aSJLaspp7Y5kFX6mUalc98XV5YRjCXa29XEyHMscI6U1R3rsofbvyi1WTUfjOwfj9EaTbF5Uw4+ebeXPNy3lO4+/OKGgzlVIz4a0MUGYaaYkur///e8XahxCjsx0blyhH5y5FHJduKyB2mG7LuRyeC7j8ZgGD+8v45JmA5UqpEp/IfcMutEjtxumwfKG3KI4xWj+MlVsx+HLv9vNgzuO4S8rw2uaRJMW//aHPSyqKue16xbx9rOWTfk6mA2ffTy27TtENJ7MtEaH0dfC0ppyHtp7iPv3Do4SMq9c3silq+bz+wOT51OPjEQPDg7SMQOfcaopAiOfUZevmjfmM+RQd8TtOovbPCbdzCot3sr9HmytOdIzwMlwlITtZLpR2gkrNdlQRJM2h3oGgKEao3SNx0g/f5UShxqoK/PTXBvC0Zoyn4dIPEnC9pL2Xoom7ZSNoEE0aWRav7sTK0Vv1Ed/3GF+KI7SbgGmg4GhHOqCEWzHSPli2yTsAO7ahHt9BL0GpopQ6XfzxTfO66Mv5mVdYz/7u0M89FIjShmsbaoChpr9jJxeuS3TNa3dEU6Eo6licvditOJuQ56srgIaBpM2HsOt/LS1xqMcYgmLnzzXypceegHLcWgoD9BUEcRnqixBHYlbPH107C6gkP19MNvTxgShEEhO9yyhWLlxM/HgnKyQ62PnrWTf3j2j/q5Qy+ETjecVS+t56mg3tg7SFqlmfkUXenhzDEWqG2aSpXVrcvbrng1NI27Z1sLvXmrLfPm3dkcyOfUdkTgHu8L8atfRKV+Ps+Gzj0V7OMqLnXEWV44lh4auBdtx2NNuoDWjIoMPv3iSy1bP54d/cUHJ2qflGtkc7xn1b3/YQ9JyaK6rSDl3xFlceZKGYIRo0uTZEzVE4p6sZlYewyASszjSO0D3YJykPdQIJ32krZTzh89j0BtNUBXwZiLd4KZR+Dwmg6nouCJd4+EWY6bznj2GwXf+/Fy+8/iLPN5qsaiy0y1oTDXGMpQrsCFBmddBo+mPewBNhc8iZpnErBBl3ighv+VWVAKGaaesCYcywN2McPAZEbzehGt1qsFUNuDDULC6PgzA40frWVt/hHKfw8oak//dF2BgRDt4rTX90SRJx436l/s8JGwn1WhoyFElfcx8HgPb0SRTxaRuS3fN3vYwlrZTHScVcWuQrsE41QH3OKnUcfrljiPUlflG90kYRvr7YDanjQlCoRDRPUsoVm7cTDw4J4tcDw5O7N6QbyYaz38904pzuAtDKXZ0NAPQGOrBSBdb4Xr4Hg/X8brNkxeSDqeUm0ZkVjxMgwRwuHeQ3mgi1SnQvT764hZxy5nW9VjKn308fvRsK/u7G1hU0ZlZ8RiJozXtkQSH++aN+Xo6Mvj+c1eXZLRvKitd33ps35jPKMvW9MaSHO4O89o1nTSFeknYFg1lrhBNR3efPr44a9+W4xBJWKkus2OgwWO63tLp50+6DbpSitqU29CBrjAdkbj7B0qxvqmakN8Vr6aKctXqGG19f+SN6wK865yt/OK5XpJ2q/t8VelMbkVP1E9/zGYw6eFATxBTGaxtiGAaBj4P+EyF1/AQTxVgumN0xa+bYhJnIOkn5I1iGgnQbvt1FIT8Do6O0x31glZsXdTNpnkDJB2N3+MK4+u3aPZ2lvHb/U2k4/daKSzt1pd4Ug1yAinrwEEnu2W7xrVSVCg3+q01cctOPbacTBqOUq7jieNATzQB3RGaUxOUpGXTORBncfX40iH9fTCb08YEoVCI6J4FFDM3bjoPzqn6FacpNd/WscYzfBKiUbR0LMPfvYDm6nZ8pkXC9tDa28i5y5ZOqTEOlHbTiPSKB7gNgvpi9qhrQmtNWyTG4qqyKV+PpfzZx6MvliDpBMZc8UhjOTatPZXEbf+4+ynlJfZcV7r+/bF94z6j0u4ZZy88QlNoEKWMYalEyo3u1oUp8x7ncHhN5u86B+KEvCa2oxlIDHWHTf/rNV1vap/HZN28cir8HuK2w6HuAeZXBjMuJ8vrKjCVojuaoCbgJeT3orXDpqZDrG4YYGl1GSf7+tBaY9k7OdFvcKK/kkWV/TBsMg2wq6OS+/fU42jFFSu7cdB4lKIm6KHM47j2oo6JZadzqB0M5TbL8ZlJwKHcZ7n+3bhxbw1YjiLotajF/WxBrxudN5yy1OdVlPkUZzZGQMFDL81znaIMA3ALNodja7fIM51eMnwtZrgbeNpa0e8xMu8DpPLNHQIeg95YkoTt4DMNPB4Ty84W8yNJfx/M5rSxuY7YARYOEd2zgGLmxk3lwZmLX3FnJD6r/VrHmoTEbT97u4aidKcavSm1yQcMTTZsremJ21nL+GkUYGf8kad3PZbiZx+P9LWQXvFoCnXiN+OpgjoIx73s7gjxSGsTqxqcUV7uaUp5iT3Xla4/tHaM+4xqDAWIJSOsqImQsA38nrS4G3qoaBQr6wZoG4xnJihJx8Hr9VDvMXEcnSoGdLOqvaaRcQ6xbQePobjyjIX89QVruGVbS9aKidaa5XUVvHlRLfMqg0TiFg2BXcyvsPB73Aiu1ppDPQP0RhMEPRYxK8R/PreU9U1dlHsdko6X545XM5D0EvQ6JB1NyO/gMz2sn1+NtsOkUvgJeExi2Fi2RmvDLYBUGnAo9+qUw4mdEb+2dvOrFSqVN+6gMVP2q1AZ9GW6/fo9HjY0xdjbZVBXXo3fVIRjFm2RGNFEmM3ze/CbNpGEweNHK+mPe4cd49F1B+kzYBqub7l23PvaPY06c2zSk+mGcj+dAxM7MqW/D2Zr2lg+GenbPRmns6+34CKiexZQzNy4qTw4nzu8bVy/4mPdL/J/L53k/r2Ns9qvda5Gb9ICE9yl57H8yTVuwReUtpDMF5lrwXHoiSWo8Dt4DI3SYKcanCRsh1jSYldbb1Z+7HBKeYl9vJWuSDzJwZ4Ilu3a0q1pqEilQQwx3M7zrPm9KJxUfjMZr2xwI69ew8A0NM3V7ZkJrN80qSv3o4CugfiYLi3pay59z+WyYhJNhNm28zHaIkksJ47HcD2ve6MJLMchacOymjC/P1TDo60NJG2N13TTNsBdlTGUxjD8NFW4edT90aFyRQUEPSaO6bqT2E4AwwCvkXZXGRq/rZXb3TKFx3Bt/WzHQWsTW8OCyjIWVJYN6/ar+PYbG3j58ku4bftu7m05xBUrThLydaYSR9xn7sZ5YfZ2lvGrvQ2ZAtB0IGTk48t2XB/vZCqI7d7qQzn06cl0mdfD69cv5k+HOnMS0rMxbUwQComI7llAsXPjcnlwpv2K090pR3KkL8pgrA+fUUVcu5GsmfRrzZfry1yN3qQFZlpkaXuMSLdSNIVci8RSFpL5In0tvHTyEZZU9qG1oifqy5qQrW0YwFBt/N/BBaPyY9OU8iRt5CTT0ZoXjvcQTiTdesFUCsOTR7oo83rYvKAGpVQmajxk4WelOic6oMBnmngMlekGGUj58Lu5z+59dM36RTx9tButGdWBNo1SirqgjwtXNGbdc+OtmNiOwzcf/TXxZHdGiDpaE3HbT6YEsZv1vKGph+2HGvAYpAoTdaaFulKKZfUbWVqzCwBjjNQiQ7m2fdXBWkzTQ/9gF7ZOopSHeDJC0nGffx5DZzJY0gsAGsWg5cXngfZIjEXVZZluvwA+j505Pwfbt1MT6CJugdbGsPfXrGt0XV3u39sIuFHrsQIGbpqJgSdltTjUbGxoYpN+tqVtA3MR0rMxbUwQComI7llAsaOruTw4D3Q8n0kpGUnCchsxGCMiWcPHXqic9EK4vszF6E1aYD6w+xg1fpOwNbpIqybgzXxRl7KQzCfvOGsedz0WwXbUUCOWFEqBoQxW1Q3w2JEEccuXlR8Lk0/SplsfkS9GTjJfON5Df9xdwXCdOUjlDiv64xZPHOmiqSJIT1ogK0XMsulPpI323MJKtE2Zz0O5z0M4nvY41yRstx36cHG3bd+JMTvQOlpTV+bj6jMX5nzP3bKthY6+HhZUDPluD6Y8s01lu6sVym2IUxVIYNkOXtOgzGPi8xiEfB6UUrx50xJuee3ZPHc4xvGeF/F7yxlMRLLeS2uN3xvENN2vWdP0UO6p4kQ4QdSKE/AkUgXYKVK2kzYQTRpoDLf1u+OM2m/a/7/Cb7GyNsLJsBvEcFLXoGKoS+bqukEq/UlsJ0Dcthlel2oqMj8nHY1HpSP5aZ8V9yjNrwhy2ep5meflVIX0bEobE4RCIqJ7FlAq0dWJHpxJOzZuS/T2SIx0P7d0JGskhcpJL4Try1yN3nz2sg0kkhYP7TxEZcCgL5pI5X8qalJOEVC60f5CeNz/767fE/AqtPYQTVrotG+7GhJ1pqF5+cI+Hj3UgOM4tEViLKwMTjhJy6U+YqZIj+/Xu44RTgnudEtyQ6Vbn7ve2DHL4VjPAKHUKkfcchuzPHGkis3zwsPs/IxMx8Qzm6ppj8RIOpq1ZWfxhdeszdxHwye4S2rKmV8ZpC3spmmsaarku285l3mV5Tl9jl0nevjB0wfZMs+hLmjjNd3ouuM41JYlU41uhsLNZy0Ik7A9/HpvPV5DUe71sryuItOAC8ich+Pd+zHwoLWNrd1mOB7TS9BbmXoPmyW1Z9I9cBzLiTGWxSSQ8RBXuM+omGXTH0tytHdwqMvpMP//Ax3P4/eqzKpB2nFF63QcH5Th8PKF/Tx62J+ZJLnfI26LepV6P7TGQrGgMsDCyiAdA65V45nzqviP684f9WwTIS0IU0dE9yyh1KOrXjMwZnEduCIs7feRsMe+5AqRAzwTjX3m0peOaRj8zSVr2eCP8sMDUXa0h4kkLJbWlFPh96baepfG9TicyVY73rt1FT95/tAoMZ6LSI9b0YzoNFOCZyQKg6ogrG2qcr2oq8q4Zv3iCSdpLxx5eNz6iOM9LwKwuv68/B6ocUhPMv90qAMz1RrdSEVlh9/vSqWFW0qUKzd6qoC47eNgTwWr68No7VrX+T0mvdFEKmfZz4KaVWxectaY730qE9z0+f/B0wdoD8f4Y6KC5TXdJG0LFNQGkwS9VsYa0E14NhhMelnfOIABPNK6gC2L6vjmtVuz3tdQBpuXXMaiyvX8bud/EacPUKhU6/jewTb83jJWNJ7FpiWv4rGXHmQw8QxBTxLLcYWvaehUdokmEvegMQh4LXpjFhoTy9R0DMRSXU49vLx5aLUjacewHbd7ppM6ru5z2MgUm0aTNiG/+wz2GgZec6iVvOU4eA23iU/CsjHUkL/50ppQzh1+i70iIwizBRHds4RSj64ub9jE0e7djC7RccVtuklEa2/jmH9fiBxg6YiWX4Y6UrbhLyujuTZENGnRNRinwu/lmvWLefvZy0riehzOeKsdjobvPv4S//HkfpZUl2fE+H07DhO3HToiMRK2K0rqy/1jpiT5PUGiCZ0S3tmOHEM4JG0Pfo/Jwqoy/mz9Ym6Y4HqbrD7CMEza+g6ypHLjqR6aKRFLupZ4hnbGjNOq9H9qKJLtNQx8KfH32/1NAKyoHWpprtG0ReK8bOm6CaP3pzLBTZ9/y3afBQMJL/u7Q24TGu0Q9NqoEZ8oapnYjnuO1zQM8mI33PnmreNe2we7nkVjU1M2D9DErEimDsdnBtHAPz7Uwvb9igsWmwTMlKOJVriZWopo0ktPzIujoSboEPLbRJNmyqXF9f/f1R6iI17PotpUx8dkF46dzLyX3zNaHAe9Bl7DT9DjwSEt8N1JUn15IFPcmy58zWVSmCaXFZmpWqcKuSG2frMTEd2zjFKNrgZ9FTRVLctE54bTGArQNTDIiUjduH7FhcgBlo5o+WV4R8r0cQ16PSyq8mA5DuF4suQE90SrHYe6I/THkijcnFW/x0QBzx1z85a9hiLo9aC1pmsgTnXQRyKVy55OSXrN+gv54RMvATrLkWM4jjZoH5wP5HadT1QfMbRPh8M9O4HKXA5DXqgO+nCcsQX3cBQQ8rlfLXpYkrtGsW3/PP54OMErmwdoDBkkbA+OsYHNS15ekDEPP/+moTKrcekJwFnzu7NylzUQTZr0xoYCAEo5XLxiYMK8+47wIVRKXJqmh3KzOmubx1tbeHT/ChwdpCdWhSJB0JNEKY3tQDhu4mCm7CY1PVE/HQMhvJ7KLP//mO3n8L6DPLj3JB7DIOgp47xFDjHLbe0+OuKt3O6e1lL+6hXz+FnLERxH4zENN11lmI1lrpPC4eSyIrN5yWU57et0RCwAhZGI6BbyRjpSNTLq4feY+H1LeK6tjrGsiguVA1xs15fTiZEdKUdSyGLYU2G81Y6E7dAdTZCwHLR22NfRz+qGSo70DhKOJ1Oe5G5jkfTfdg/GAbI+55LaBoK+hcTihzGUkcmtTaPQHAtXoynL+TqfqD4is1+lSDoxjBkQ3enVtfkVATfneJw0MndcEPAYGevIMl+Ss+b3EPTYRC235ftAwsfh/gbaBk0crVkzv3BpCMPPvzv5d89hegLgM23WN/VhKjeS3B83UwWMQ5/Pa5pcu3bBuO+RniSNR8Jy6IvGWVnbyd6uxSRsL16Ph764yjScSreJ19rtKhnym/TGK0cVnR/qjmRcXBZXlxO3/XQO1lIT7HDbv8ezV1qUctjVHiRq+7jhwjN5MuUGMx5TCX7kuiITTYQl1UQQUojoFvJGOr9xeH6fzxNkWf0mLltXRtjKzWYqXxTb9eV0YnhHyvEoxVSdsVY7NLCnvY+BhJWxaYvEk+w40UM0mfLBS22XsB0Cw7zHe6MJBhLJrM/54Yveyh2P/Jho/Bh+rwFJB8txcLTiWLiaw30r8Zgq5+t8ovqIzGfQGq8RYOLegKfGWLnwQa9Jf9zCIJ1SM7S947gT2JoyP/VBL2cvOEKFrzPl0OEmNaxr6ONYpJpj4Ro0hb//hp9/174v23qwP+6lL+aB1GdJpyBlosSmoq7MR235+KJxskmSW0g+VETe2ttIfbANACMVfQeFaSg8hqbMk8Q0k3gMC7851CwoYTv0pptUDbsXd3Q0s65BU+7txNG22xUTN69+f1cFTx9fzIIqm+88/mJeC/JzXZE50PE86xZekNM+BeF0R0S3kHeCvooxH7IznZNeKq4vpwPDO1KORymm6oy12nGoO8JAwiJlyewKLMMg6WgsnS4STMU6R3xerTWdA/Gsz+k1PXzs0r/gcHcHv9qxnbgVA7wMWs2sLAtw9tKpXecT1UekMZTBkpp1HOw+kvvBmCJj5cJvXljLE4c7iVluc3GPUjiO2/ClMuDlzKZKLluzgCrvDuKJHqJJheVA2pPD51GsrQ9TE2jl2balBb//Rp7/kdaDz56oYV1DH4bpOoD4TTfFIz1NqA76aK6pyLiFjEV6kpQmYTnDGtm4jXfUsCLycMLL3q4yVteFQQ/VAtQGEwQ8FqAZTHqZF+pmfqiXtkg1OzqaaYvE3MtR66xGQRrF0yeWcrinnE3zugn5NBofezrqqAhUsrTWjURv39/O3W87L/P/TzX4kfOKjD1xB0tBmEuI6BZmlJnOSS9115fZwvCOlONRiqk6I1c70tHCdN5pWjJ4DUXCdgW3k+pVkqkKHIZSCstxxvycS2ob+OCFbzrlMU9UHwGu/dyCmlUEfKEx/jo/jJcLbyjF1qUN7Gvvo2MgRtBj4PeYLKkuo7oswIUrGvn4hcv4w4tPErdqaItE6RqIE7ccvKabOqGBplAvV1Rt4jMFvv9Gnn+lFM21oaxumScHarj6DI3P9GaJ5caKAB6lmVe9bML0iOUNmzjUsQOt4XDvIH2xJEOxfdcVxHbgYKqIvC0SY9fJJtBuUamhNDWBBEGPjVIGkYRBT9SL19T4PZr5FV0AHOxuSF2XbqqM34zTXN2Oz0zSHtEc6i7nj4eb3IlCbYi6EZeH5Tj85PnDeQt+5Lwik/IUFwRBRLdwmlPqri+zheEdKcejFFN1Rq52pKOFvlTnPVcUGJiGgXLsjPmITmX2+kYUIWit8ZlmwT/nePURhjJYULOKjYsvJRYtXARxIucfBaxprGKpFWJeRZDNC2uz7qmdx36Pox18HoPF1eUsri4fFf1tCPlZ2WhNuTHVVBlvtcvvMVlcXY7lOCxrPJ+VjV209R1kYVUw61jn4oke9FXQULGUl9qfJWK7E4v0UVOAz1Q8117Gvo4kzbX+VEt1N6f8sSNJti7qYOuiBHHbR9z2MZjQaJxhDi8GjaEeKgI1dA1q6st8nDP/ME2hXpRy91Xtt2iu7mJ/d4jn2paOOc7hK1H5CH7kuiIz0SqBIMw1RHQLc4JSdX2ZLQzvSDkWpZyqk7XaYdmZtBGPodAo/Clh7TUUyVSTF63BY6rRzb2V4rJV8/LeZGckE9VHBAsY4U6Ti/NPwGOyeWEtN1+RbV04VtqBz2NktTFPbzddptLoKJfVLtMwcjrW471vQ+hl7Ol4kUU1g64VYCrO7WhFe6SOp4/X0x1NsMB2ME0jEwkPx70MJgx6Yz68puuhHfAakEy7gKQXXDSvWNRPOF7La1a3M7+iG41Buv5AKYWhHFbXh2kMtbGrc/mo45DvlahcV2SkiFIQhhDRLQhCTgzvSOlojQmzIlVn+GrHB3/2OM8e68JrGDSEApzoj9IbS6JTjX08piZpOQS8Jn6PkWqpnfGWoLbMz+72Pna29Y1qspNLE5GpMl59xFSYTifOU3H+mSztwI16R9nV3sUjrbunNGmZrNHRWOcg19WuiY71ZO/rV5onjixgf7+XFbWdWTZ/cdvPkhqNoyO0R2I0lPvpisSIJi0sRxPwus4nSdshaeuM9d+q+kp6oonM6sCFKxaxuLYaZe1LCe7hx9wgaWtMpZhf0cv+nvgoe9bprETtOtHDzb95nt5oguqgjy++ehNnzq/JvJ7Lisx0KUQH2dmCeHCfvojoFgQhJ9IdKS+sSvJ8LEjUUbMqVaexIsidb97K237waCbHt7k2RMJ2W7PbtkOdoUjaDktqQliOQ8dAHMuy8XlMGkNpEaNI17G5Thewbd8JYMi/OxcKLSqmI1DTnIrzz3hpB1prDvUM0BtN4Gj4/eEyotbhKU1axmt0lMs5yEeDnfHeV+Ee37jtH2XzB24kelldBecsrmNBVRn/un03g0mbMq+B5aS/ht39Jh2HACblfg/lfk9mkvL44T5q/ANor4dw3EJhE/QmSVcnmMqLo02U0jRXt2eNY6orUQnL4qp/f5jnj3Zj66Fr55GXTrJpUS3/+1eX4vN4CrIicyrX7UwiHtzCdBDRLQjClKgNevnQWasoKyubfOMiMJGYHSvH12caLK5yP0tanLz/3NVZUdHLVs/jhl88Oa4InYpP+UyJilMVqNN1/hkv7eBQzwA9gwkM5dCeapRluJ3jc5q0TNToCArnFZ/L+x7tjRDQNhPdEY7WLKgq47otzdy303Wd6Y0leeZEDesa+zMTOW/qfeKW7a7EDJukrKo7xvxQkppgEq/hrtDYGkwFIZ9N0vHSPeglmhiktTuCoRTzK4NcvmZqK1FX/fvDPH2kC1Op7GsHePpIF1f9+8P89oNXZLbPx4pMmlO5bgWh1BHRLQjCaUGuYjbXHN/hUdHbtu8et7AwTa4+5TMhKvIhUE/F+Wdk2kHS1vRG42gUJ8J17OhonvJ4JiruTFMIr/hc3re2zMeJngQ1424xtDLwo2dbcRydtcpyLFzFito+PIbHnYgAe9v7U+87NElJ2l7KvTHQSZL20Lm1NLg55DH8HouYXQW45juTpOaPYteJHp472o05zh+aSvHc0W52nejJSjXJB8WaWAnCTCGiWxCE04Jcxex0HG1yKSzMxac8V1FxuLuDcHQvSTuG1wywvGHTlArS8iFQT8X5Z2Tawfb9RzjU6+FQX9OoXONcx5OvczBVcnlfv8dDY5kHy3bGXKUYvjIwfH/pVZZj4dXUBFppDPWQ7kw5mEzi83iyJiknwtVsajqIo92kkuHC2NEajabcm6AmEOXKVT2pnHJjSpO5m3/zfE75/Df/5nn++90XT7q/qVCsiZUgzBQiugVBmPVMJ0I2lRzfUyksHM5kokKhObP+AA/s3MmiqiCOYxNNRth9/A9UBus5f9W1lPsnb/2eT4GaPk5p8X3nH/flnIOeTjv4753Ps6+77ZTGk69zMFVyfd9XLqygrKaePx3uZiCRpHMgTtJx8Jsm16xflFkZGGt/GkVLxzL83Qtorm4nmhgkZnnoSyzKmqTMr+glbht4Dbebqo3rtqOUxjR0qp09LKvpI+nEWVzZmWmuk2uEuDea27XTG01MfvCmSLEmVoIwU4joLnHmcgW3MPuZqeu30BGyUyksHM5komJ9QyvzK7qxHT8D8R63u2XKCaS9/xD/+8KdrGw6e1JXiHwK1HzkoOdjPPk6B1Mll/f1KsWrm6t4xeYz+NLv9nDvjiNYtoPPY1JX7ufpo93csq2Fz162YcyGTelCXtM0CCcWcqJ/kHkVQfyebCs+txGOj6qApsxro7TGUQoPDqYCRyuSjsJQmqSd3Vzn+fbmrOt/vHuzOpjbuaoO+k7twI5BsSZWgjBTiOguUWZLBbcgjMVMX7+FjpCdSmHhcIaLiuEdBRO2lxPhappCPTgYeI0o8WTStd3LpMoYxJJRnjr0HP/30kksZwObAmN/nnwK1HzkoOc6njesr2fnsd+PmVaTr3MwFlMtvh35vhcua6A2CF99ZA9/OtTBwqrsksqRx+qiFU08tPcEx/oG6Y0lsGwHy9FoDUd7B5hfEcBjjL6eT4YdltU4dA/66DMcKv02HsMm5FPE7aH+9Y5WmWOdbq4T7F5Afyw56b3591ds4JGXTk7Q8sY9/198df6LGYs1sSo1zBu/n/WzWAiePojoLlGkgluYzcz09TvVCNlwi7Ncc6ZPpbAwzXVbmrl/52HW1R/M6igImjV1RzCUJpr04TetLJ9rDcQsG8t2iNsDDCaO8n+tIX7ab3NFn5e/v2pL1iQmXwI1X4Vtk43Hdmxed2YXu4/9Msvv+Wj37kxXSEPlVgSbHncuKyz5Kr792Hkr+dNzLfzhYG9Ox+qzl23g//a30T0YJ245WI6D1mAYChPoGkjQG02yaUFN5jqIWzZ/PFxBc3UXCo3tGPTFTar9rpd3Bq0Ixz0Mb6ZqKE1zdRuVgZU53ZubFtVm3EtGHTOtOXtxXd6LKKGwEytBKAVEdJcgUsEtzGaKcf3mGiF76+YlPHd426hmHiPF3VicSmFhmsaKIK9Z00Es3gVqqKOg6/2t8ZkWQa/GbSQ+JHhcwe2GMV1PaM2Kmg6e7q/ldy+14dvWMmoSM51JwkixGo4l8pa2M9F4XndmF6tqw2jMjMh0OzJqjve8CMDmJZdNeg5sx+GLDz6f8wpLvopvBwcHeeBQP5bWTJT4kD5W121pRgGVAR8dkRhe00Ch8JpGxjowmrR4qbOfVQ2uE0l7JEYk7uFAd4iVdWH3GOEKapRB+uIftAwcbRAYrrpR+E2by1fN44ZfPjnpvfmfbz2fd/34Dzx3tDszmU3/e/biOv73r6bf9GYy8jG5nQm++4adk24jXt7CSER0lyBSwS3MZopx/eYaITvR+1jGP3oicTfZe0133NFEmDX1gxzqCdAbTWTafLsdLxUeU+E1bFBDj2ZHkxLcQKoRCii8pg2Axxx7EjOVScJ4Ed8jvQNoDUtrysftMJlr2s5443nDhnp2Hf0lbo/TMfZvmLT1HSSaCGelmox1DqaywpLv4ttI0sk5xelHz7YymLCIxJMEvWN/7oDXQ9SySdoOpqGwHQ1K8fDB+SilWF4bJt1qHhw0isGkSU/Ui89UWWPRWrOkppqHXjyZ07353y2H+e0Hr8jqSFkT9PHFqzaxdl7+I9zDycfkVhBKFRHdJYhUcAuzmWJdv5NFyD5+4TL+8OITWQ1bssY0hrgbyXTSUoZzoON5IOXRbDm0R2KZNt915SEG453YjoXtWHgMN2aasIelDqCIWV5Ak7SHPsdEk5hcJgnjiVXDMGgPRwG3e+dYTLWwbeR4dh77fWbVYTwc7XCg4/kJG7BMVUTne3IY8ho42s5MHUYWSDaF3DztyoCXvliCzoE4OlUkOxYKqPB5uXLNfEIBHw/sPc5LHf00VQTpiNawa28Py2vaqfQl2DCvn0jCg+UoDENlCjDTk7rqMj/v2HoV/7z94JTuzTPn1+TdFjBXTmVyKwiliojuEkQquIXZTLGu38kiZKci7hzt8MKRh6eVljKcpB3LvL/PY7CoOrvgzrYDROMD6GFDTLdTV2jithdHGzgaDvQ0AhYw/iQml9zmicRqUyhA10CM3miCuGWPctOAUytsaw9H2b7/CEoP4DEMGkMBfJ7R41BKkbRjE+5rqiI618lhJNY/bnHncK5cWskzvb1o4FB3hN5YAq2HVjK6BmLUBH38+aYl/OT5w3jNGK9s7iDosYlaJs+eqGEgMXRPaMDjMXFQfPTCtVy3pZm3/eDRTApVY0UtXbEqdnfGMMyjrKjtI+j1sLqhkp5oIjOZawh5WVq3hlCgUr5bBKHIiOguQUqhglusCoXpUuzrd7wI2XDBOx7jibsXjjx8ymkpAF4zMGF0s9xfg9aQdOI4qWh9esu47WUgGUAx1KEwLbpHCqWpuMdMJFZ9pkF1wEf3YJz2SIzF1eVZr0+3sG34+FbXDrCkyu1W2TkQozroG5XOorXGawYm3OdUV1gmE6AKzbqGgzQGYxzuCmZNtEKBxTx1YjF9MYuqgI/Xn9FEbdDL+c0NfP8Zd8I3PCtfAY6jsR3Ndx7fx6XL2ug58yCWY+E6bWvWNfazvzvEb/c3oVEopWgo92fO61gpVH6PyeLqco6FV1MVOMjaxkHKfCbl/jK01hjKyEwKofj3piDMdUR0lyDFrOAWq0LhVClVB4LJBC+MLe6iiTBtfQdPKS0lzfKGTRzt3p2JXo9EKUVlsI5zmq/m6UMP0B/twDTgUK+NoxWOhvZIukPh0D5GCqVcc5vbw1Ee2HOMwz0DmRQIn5l9zpam0kocR2cV1Z1KYdvw8bX2NrG4sgul3M/TM+g2XRmezmIog+UNE7vdTDWKO5kAdT3Te2gK1WRdMwe7w/RFn+F4uJUdHctxtObelkOsKdd84oqX8V/PHXKj26lrLf1vbZmfpTXldPc/QWfEoiHk50R/OnVIYShYXe8WSD60fx41AS9lXk/WeZ0ohaqh6lzesXUZh7peIGnH8HmCLKvfRNA3dBxL9d6ci4gN4NykZEV3b28vf//3f88TTzyBYRhcdNFF3HzzzQQCo6MdP//5z/nsZz+L15u9JPbDH/6QjRs3ztSQ80qxKrjFqlDIB6XoQDCZ4IWxxd2BjufzknMMbofGpqplmaj5qP04NgtqVlFXsZAr1v9lJof8xMAh9rRHOZzVRt39HJbt8KphQimX3OZH9rcR+Z+nePpoN4e6B+gZjINSdA3EqA74WFobyorSLqkp57JV8wgFfKdc2DZyfHHbT1ukmvkVXWgMNJq2cAzb0fg9ZiY9YrIJzVSjuBMJUL8Zp7G8h6qgPyvd5VDPAD2DCZQyaAr18mJ3nLjtx3Y0j58c4CP3PsOS6nLmVwRpj7ifwWOoVNqMid+M01DeQ0ckwPK6EOF4knDcyhxrrRUraiPs6oCasjIuXNE45eLYya7BUrw3BaFYzLTWLFnRffPNN5NIJLj//vtJJpN89KMf5etf/zqf+9znxtz+ZS97Gd///vfHfG02UowKbrEqFPJFKToQ5Cp4R4q7U0lLGYv0Uv/I/HBDGSyoWZXVbTLdRv2M+edxy7YW9ve04+ghoWQoxSUrm7KEUi65zQc6w7R2R1haE6KpIkD3YNx9QUNPNAHdkaxIs8cw+KtzV+flvh9rfDs6mtFoqgLdOI6NBjoHYnhNkx1tQeo66tiwyJlwlW06UdzxBOjymnZqy70srRlKp0lYDr3RRLpXUcr7up29XYsxVZSV9cdRxhGU8tMVWzgqFQegubodQ2ksxz3vmxbUcKArklVU6TXhomUDNFWvHVcAV/gtLlvRlckzD/kWTXjMh1OK9+bphlgFzh5mWmuWpOju7Oxk27Zt/OIXv6C2thaAD33oQ3z0ox/l05/+9KhZxunMTFZwi1WhkG9KzYFgKoI3zWRpKa4LSZRd7V080rp70voHQxlsXnJZlhPKWKkAwxlLKJWZsCkwyHlb1maJ0clymxO2Q188SXXAbePt95hUB9287XQOeW8sScJ28JlG3lMOxhqfRnH/3kbiVhlnL+gl4LFRyk9/YjFx2491sg0Y7UU+kqlGcccToGfP14Rjdta27ZFYxg3EReEzkyyq2MvCij5UyrYPwiynm2PhKo6GV2f9hc9MutaQqfOllGJFfQWLq8uznGwuWrmQ81eN/qz5KuiF0rs3BWGmKYbWLEnRvXv3bkzTZM2aNZnfrVu3jsHBQQ4cOJD1+zQnTpzg3e9+Nzt27KCyspIbbriBa665Zkrvq7VmcHAQgGg0mvXvXKAzPIDWGnui9VmgKzyQOU4zwVw8F6XK6XAuVtefx5LKjRzu2UnSieE1AiypXU/AW04sOjpaPb98NYc6duDgjHrtcO8gfbEktgPbDwWIWq3c23KI85sbuOmiMzDHaOU9hMmymrMyP2kLBq2J76uQCX91zlLAPQetra2jzkWZAUnbHld4n+wfTE2u3RoOgEVVQRyt6Ysl0an7/0TfAEury7lwWQMfO29l3u75scaXsGw3wo6PRw81ooH6Mh8Lq7yAgwL+b98J3rl5MQ2hiQsqb7xgFe/cvJifvnCE/niSqoCPazcsoj4UIB4bezVi+HEF2Nd2kh4ruyto0rZBD8+k15R5uqmotN0rQyuUclNEDAVLKntB7+VQ/+rMX8QtE6U19WVebHtI1JsK5le4aUNaa8o9/jGP984Tj9DWfwA1Qlg7OBzt2ksymWTd/IsmPD75pCMS46cvHKEvnqTK7+XajZOfn0IzlWfUZDUehWQmv0NPN/J13oqhNUtSdPf29hIKhbIOalWV25Wrp6dn1Pa1tbU0NzfziU98gpUrV/LQQw9x00030djYyLnnnpvz+yaTSXbv3p31u9bW1ul9iFlItLebSCQyaSHSYK8x6jjNBHPpXJQ6p8e5qMSgEhs42H144k2TZQzYbVli50QkQX/CwVCao72V9PRbpN1EftUSobOjnfduaCzc8FOMPBebAkl+Ojg4buZ6NJ4Ex6Fc2QwODGR+3+CDKtOkJ25jO5p5AcXfbKmmJgD79u7J23jHGl/bYBJrmMhVQMhwssbnaM3tDz3FdWfU5fQ+l9Sk95Sk48hBOqYwxqTjI5qMMVxi21YS27Yz6SVaOxjKwdHDBHDaIlC7lQMLKvp4sb0bK5WH/0KsnFXVnRztCWM7YBpQG/DgzZqcKWKWj93d2c/YpBPjSHJ31phG0hrdjd1didcorPC1Hc1dOzt4pm0Qe1hx7U+ffomzmsp497qGSSachSfXZ5TP5yvsQMahGN+hpxP5OG/F0JpFE9333nsvN91005ivffzjH89EW3Lh4osv5uKLL878/JrXvIaHHnqIn//851MS3V6vl5UrVwJDUaTm5maCwbmR5/aRxTGe+ekTOBMcehP4yOXnzGg0Yy6ei1Jlrp2LWCLC4Z6d1Nv1GAMJkk4chcJyNIN9CQzD5ORANXt6llJWni0y9kWgfvGygt0rE52LK/q8/O6lNjzm6FQDf2IAj9dLdeXonOMyoBpX4P7Z2gWct2VVQcY+cnxGYgBP6ttIa0110EdVxRjjq65l7dqZSYkwT/RnRZUX+R0iHeGU97ZDOOHWBaStI3EbRhL0ekjYTmo1waG5vpej4WV4AEeVcSjcz7xQN2CArYlYFlUBL0uqy9DaoalyOevmbxk1nn1tjxPsDUzqvhOoTrC6afTf55Mv/243LX2aYPnoc9TS5/A/7Yq/uaQ4qStTeUa99NJLMzSq0czUdXw6MpXzVmpas2ii+5prrhk3JP+HP/yBSCSCbduYpvtg6+3tBaCuLrcox8KFC9mxY8eUxqSUoqwsu1lFMBgc9bvZRq6e20vLyrhk1fwJC5EuXj2fpY21MzHsUZwO5+J04XQ/F2PmziqNqQw8Hh9Huss41OfnUMpNZKz6PkdrfrmnreB5s2Odi7+/agu+bS1s39/OYNKiYyCOZdl4PSZXn7mIXW29DM81HolXKd65dTVlZYWZWA0fn+U4eE3Dza03DGqC2e4paRytqason7Hr7uzlV2ZdA0YqbSSSsDnSV4nt2CypjgCgcIW3aRiYhiJomDhak7QVjSGTVyxv5nj/II+1drCzYxkKRWOoh3QwuDeaAKU4d9nG8fOyTRuPJ4evbNMu6DFqD0d57HAXfu/YYzENgz8d7iZiq6IW3OfyjCpGaolYBZ46UzlvpaY1SzK9ZO3atWit2bNnD+vWuVXALS0tVFZWsmzZslHb/+hHP6Kqqoqrr74687v9+/ezePHiGRtzKTIdz22xkxKE8ZvhKNPEshPELT/7updMuI9CtLrPFdMw+OxlG4jEn+GXO45g2a6wrS/zsbu9D5Qiabm/G0m6cBLgtu27C9Iga2QB4/G+Qe7dcYSGMXzC08x005Z0wWsk1s+//+F/OdTTS9yq59FDIY73w7mLTrKoyp2QKRSmAYFh9oKGUvg8Bmsa63lTqpukxzDQQEvHMvzdC2iubsdnWiRsD3840sSbzjp/3ELI6frM55vpFNxLszWhFCmG1ixJ0V1bW8uVV17Jv/zLv/CVr3yFRCLBHXfcwZvf/ObMTP9d73oXb3nLW7j66qtJJBJ88YtfZPHixZxxxhk88MADbN++nZ/85CdF/iTFZTqe22InJcx1cmmGU+7twmtUknTGFzjFbqd9y7YW/nSok0VV2SkAWrtFgV6PiYEaNbm+dNU8HA1v+8GjBW+QNdxBI+T3ZJ5LIylm05b/t/0g2/ZV4TFqAGiqgJoyh8P9sH5eGK9SlHlNtHYYtXqgDV6z/sIxhWrc9rO3a+jL2tF6Qmeo6frM55updP6UZmtCKVMMrVmSohvgH/7hH/j85z/Pq171KrxeL6997Wv5+Mc/nnn9yJEj9PX1AfDOd76TgYEBPvrRj9LR0cGiRYu44447WL9+fbGGX3RO1XNb7KSEU2G4HZ7XDLC8YdOkzU1KhVya4TSW+1hR28GezvEjHMVspz3Z/e81XcF92+vP4aEXT2ZNrr/5x31FaZBViqts4x1Hn2nQWFHLyUg1S6p6x/5j7RD0L2FJbQN9seNTalE/FtP1mc83U+n8ORearYkn9+xmprVmyYruiooK/vmf/3nc1x9++OHM/1dK8aEPfYgPfehDMzG0WYF4bgvFIJ8+wsUil2Y4fq/JqvoAO9qdkmynnev9v+3Fg1y2YjAzOYom6iedrD+09wQGYENeUwVKcZVtsuN4NLwa9F6aayJ4U23s3aJKg6B/CR++6K3A1FvUj8d0fObzTa6dPy9fNY8bfvmkNFsTSpqZ1polK7qFU2MqS4CCkC/Gy4XWaI73vAjA5iWXFXOIk5Jr7uxlq5cQtipLKjKbZrL7X6HZ1NiKV+/kcFf50OSo7ynW1QfY2bEs1ehlCK212wY9muBEOMqS6vKCpAqU0irb5M9RxdHIGTRV+vAb+/H7vQT95bxuw0UsqqnPbDXVFvXjMZ3GSvkm186fD714UgI/gjACEd2nKfmKrAhCruSSC93Wd5BoIlzSqSa55s6uaNzMzYsqSioym2ay+399QyvzQl14jbKsyZHtOCyo6EahaOnILiQ61DOQ6VrppHxFT7dUgZHk+hxtDNVwSc1m1q5dO6ZjxnRa1E9E0FfBuoUX5P5B8kwuqUD/uK1FAj+CMAIR3acp+YqszAWksj4/5JIL7WiHAx3P5yQY8pkXPpV9TTV3tpQis2kmuv/9ZpymUA8og8YRHuIew8DBoDHUg797AfFUU5e4ZdMbdaO+GjBHOIycrqkCuT5Hr924mM4jByfcVynmrE+XXFKB5krgRywAhakgovs0Jd+RldMRqazPL7nkQiulSNpjt+JOk8+88OnuqxRyZ0+Fie7/5up2QFMd9OPzZL/WGArQORBDKU1zdXvGXaM9Esuk3CilaBqj4c/pmCqQ63O0IRSgc5J9lWLO+qky0YRTAj+CMBoR3acxp1NkpRDMhcr6mSRfPsL5zAuf7r5KIXf2VBnv/vebFjVlfpbWjO4m6PMYVAd99Awm8JlW5ve2o1PHDWoC3jG9tE/XVIFcnqPx2MQTyeGU4spIIZDAjyCMRkT3aczpGFnJF6dqqSiMJh8+wvnMC8/HvoqdO3sqjHf/v2KRj67IvnEnR64Y1xwLezLpAem0knS3yLE4HVIFxkKeo9NHAj+CkI2I7jnAXImsTAWxVMw/+fARnk5e+Hg5+fnOMZ+tjLz/o4lFbN/70riTI6UUy2oruXrDn/GLHZ30x5IYaB4cJ2KZ5nRPFcj1OSo1IkOczhOW6y/4p2IPQZiFiOgW5iRiqVgYTjUXeip54ZPl5L9mTTQvOeanG7lOjpbUNvDRCxuGfg+SKjABtqP58u9289jhLqkRGYEEfgTBRUS3MCeZK5X1M82p5kJPJS98spz8kNnHGQ2nnmN+OjKdyZGkCkzMXTs7aOnT+L0eqRERBGFMRHQLcxKprC8s082FzjUvvCK4hkf2PzthTv5v95ezsrYX7wRPuclyzE9XpjM5SqcKHO7u4Fc7thO3Yvg9AV674UIW1zSM+TdzhY5IjGfaBgmWjy5OBakREQTBZW6udQlznnRlveU4Y75uOQ4XrmiUL8gZJp364Dj2mK87jk1T1TJ+saMT25lgxgQMJL0cDVdNuq9SbtRTaNKTo81LLuPMBedPuBrhaIfnDm9j97Ff0lzdyRkNAzRXd7Lr6C957vA2HD32vTQX+OkLR7AnmsEzVCMiCMLcRSLdwpxFlstLk1xSH+7bk1u3u87YGZzb3Dlr/bZLiXxaOZ5u9MWTs6pGRIo9BaE4iOgW5iync2X9bCaX1Ifcc/L9s95vuxTIp5Xj6UiV34szSaS7FGpEpCGYIBQXEd3CnEcq60uTifLCp5qTP5v9tksBsV+cmGs3LuanT7804TanWiMyfOLoNQMsb9g05QmONAQThOIiolsQhFmHdLubWaZi5TgXaQgFOKupjJY+Z8xIca7X41hpH/UhPy8ceXhUitTR7t00VS1j4+JLMdTk0WlpCCYIxUdEtyAIsxLJyZ85pmLlOFd597oG/qdd8afD3VO+HidK+3jtmnZW1YYxzVPLpZeGYIJQfER0C4IwK5Gc/JkjVyvHQtsv5iPFolCYhuJvLllLxFZTvh7HS/vwGXEGY0c50uejuXZ0DcJUcumlIVh+MW/8PgD2re8o8kiE2YSIbkEQZjWSk194cu1iWSgB7GgnLykWM8FUr8eJ0j6aq9sxDE1vNEHCcvB5Rm+Tay69NAQThOIjolsQhFmDWJ0Vj+l0scwXp7Nd4URpHz4zCSg00B6Jsai6bNQ2uebSS0MwQSg+IroFQSh5xOqs+Eyni2U+ON3tCidK+0jYXkCjUOM28so1l16KjwWh+IjoFgSh5BGrs9Jhpu0XR9oV2rZFzIpkUiUCnhCGYc5au8KJ0j5aextZXNkJSo/rOjKVXHopPhaE4iKiWxCEgnMqBXBidTa3SdsVaq0ZiPcQt2KQclLRWhNLDuL3BEhY0WIPdVpMlPYRt/20RaqZX9FNY2h0NHuqufRSfJw/vvuGncUegjALEdEtCELByEcB3FStziTve4jT4Vik7QoH4j3Ek1E34j0srxsglozSET5SzGFOm8nSPp5rW0JzXTl+TzRvufRSfCwIxUFEtyAIBWM6BXAjheKxvoGcrM56owm++ODzkvfN6ZUDv7xhE4c6Wohb4zfoMZTCchKzMq+7PRwl5PfiMQ2O9g5SX+4n4DEzaR8Xr17ARy6+koQ1MKO59EJupK0Dx0LsBIWRiOgWBKEgTLUAbjyheKI/SsKyaa4NjSu6HK154nAnkXhS8r6ZmRz4mYqiB30VeDw+tHZQY6yKaDR+TxkKlcnrLmU/7zRjXe91ZT46B2IsqirnmvWLefvZy9AavvH7vanjXDcrVysEQXAR0S0IQkEYWQA3FsM9hscTig2hADtO9qB6BsZsEAJgO5pwPIlX8r4LngNfjCh6Q8US+qOdJKwoGlCQ+dfvKaPcX41SioQV5bnD22aFn/dY13vQ62FRlQfLceiLJfjmH/edFqsVgiC4yB0rCEJBSBfATUTaY3gioegzDWqDfnqiCeKWPep1y3EIBTyYk7xXOu/7dCedAz8Rp3Is0mJRa8aMot+yrWVa+50InydIub+a6rImAt5yfJ4gAW851WVNhAI1GXHdET7M8Z4X0egx05leOPJw3sc2HXKZGH3/qYP8etexGT3OgiAUFol0C4JQENIFcBMJ77TH8GTFkktrQ+iuMJ0DceZXBkdZnTlas31/+4TjmSstrgvZ7rtYTjKZNvRKUe6vGnMbjcayEhhm4f28R6avzC9fPaW/n+x6j1s23YNxDENR5hv9NT2XVm4E4XRCRLcgCAUhI5QYP+qa9hju23lgQqGogGV1FbxscR3zq8pGWZ3dtn23tLhOUch231N1kskXubSh95h+bCaeSOTaMn2ivx/LjedQxw5IlrFGr8lpP5NNjNojri2ibY/dEAcKc5wFQSgsIroFQSgIuQiltMdwrkJxflXZmCJDWlwPUchjUcgo+mRM1oZea01b/8EJ95Fry/TxGM+Nx8FhwG5j98lHedmKqybdz2TXu+1oUArTHD8DdK6s3JQq7/nFumIPQZiFiOgWBKFgTCaU0q+fqlCUFtdDFPJYFDKKPhmTtaHfeez3OaczTYfJ3HiUMugIH84pfWWy69003M/QNEZDnDRzZeWmVBE7QGE6iOgWBKFgTCaU0uRDKEqL6yEKdSxKYUVhvDb0U0lnmg65ufHYOaWvTHa915W5kxvfBJHuubJyIwinEyK6BUEoOOMJpeGcqlCUFtdDFOpYlPKKwlTSmabDVNx4cmGi6/3qMxfhaHj4xdI7zoIgTB8R3cKcZTY00JhL5EsoSovrIQpxLEp5RSHXdKbpMBU3nlyY7Hq3HQdDleZxFgRheojoFuYc4zkQlGIDjbnISKHYHo5y2/bdBe98KORGKa8o5JrOlAsjO26+Yf0aDDVZ+oo55fSV8SZGpXycBUGYHiK6hTnHeA4E6QYaAJuXXFbMIQoUp/OhkDulvKKQSzrTeEx03b12TZBVtWHMMbzAtXZoqFiS99WyUj7OgiBMDfnGEuYUkzkQDG+gIRSXYnQ+FISJrrtf7qrjxe4KFO7qGLgpJQqDkNnE2nmvLObQBUEocSTSLcwpcnMgOLUGGsKpU6zOh8LcZrLrzjRMfrW3iTdu3kw4ujeTvjKvfDUHXzosaWmCIEyIiG5hTpFvBwKhMBSi86EUzgqTket196MLqrMAACZWSURBVIsdnXz0wqFJ+eDg4EwMTxCEWY6IbmFOkW8HAqEw5LPzYa6FsyLKhWJ23BQE4fRHRLcwpyh0Aw0hP+Sz8+FkhbPpSViputmMdNEQ95bCUcyOm4IgnP6I6BbmFIVuoCHkh3x1PsylcHZ/+zP4PWV4Pf6ScrOZzL3lY+etnPExne6UQsdNQRBOX6TqQ5hzbFx8KQtqVo3hQKBOuYGGkB/SnQ8txxnzdctxuHBF46QR33Th7Hg4jk08OUjCjo75ejHdbCZzb/nqI3tmfEynO/m67gRBEMZCIt3CnCOfDTSEwpGPzoeTFc5GkxFI7Xc8iuFmk4t7yx8PdnBhVfWMjWmuUModNwVBmN2UtOhuaWnhE5/4BDU1NfzkJz+ZcNu7776bH/7wh3R0dLBmzRr+9m//lvXr18/QSIXZyKk00BAKTz468k1WOKu1A5Pk8BbDzSYXF42k1jxwqJ/zz5rBgc0BpBOkMBHXX/BPxR6CkGdmUmuWrOi+7777+Od//mdWrlxJf3//hNs+/PDD3H777XznO99hzZo13H333XzgAx/gwQcfpKysbIZGLAhCITiVjnyTFc4qZYBSBDzjr3AUw80mVxeNgeT4qTPCqSGdIAXh9GemtWbJ5nTH43HuueceNm2a3EXinnvu4Y1vfCObNm0iEAjw3ve+F4Df/e53hR6mIAglTLpw1nHsMV/3e4L4PUFMc/z4QzHcbNIuGhPhaE25t2Qf4YIgCCXPTGvNko10X3vttTlvu3PnTq6++urMz4ZhsHbtWlpaWnjNa16T83601pkmB9FoNOtfoXjIuSgdZuO5WFn3CpLJJB3hwzjazlgCGsqkqWIFWmvawwfdqPcItHZoqlyOtkwGrZlrgPL6M5q4t+UQtjOBtaXWXLm0cladi9OV2XhfnK5M5VxM1rNhLKQRUvGZznkbj5nWmiUruqdCb28vVVVVWb+rqqqip6dnSvtJJpPs3r0763etra2nOjwhT8i5KB1m27kwaKTWqaTXPoyDhQcPVeYSjF4359uwOhlwOtHDfLqVMig36lE9Dezu3T35m+SZNeWax08O4Bnjy8XSmq3zyqkNemfduSh1kk4sc50YeKg2l+A1cksvknNROuR6Lnw+35T2O1IjCMVhquctH+RDaxZNdN97773cdNNNY7725S9/mTe+8Y1T2p+eZCk2F7xeLytXut630WiU1tZWmpubCQaleKaYyLkoHWb/udgy5m/P5ExiiQiHe3aSdGJ4jQBLatcT8JbP8PiG+OqaM/jqI3v448EOknrIp9urFOcta+AjL1/KkcOHZvG5KC0c7bD75KN0hw/heNzJl6U13aqPhoqlrJ33ynGbJM3+++L0YSrn4qWXXpry/teulTz/YjOV81ZqWrNoovuaa67hmmuuycu+ampq6O3tzfpdb28vq1atmtJ+lFKjkuGDwaAUY5YIci5Kh9PxXJSVlVFb3VjsYWTxxdecPa6LRnqZ+3Q8F8XgucPb6Ii0Ypgmxohyp45IK94u76RNkuRclA65nIvppCjI+S0+UzlvpaY1T4sqnPXr17Nz587Mz7Zts2vXrpwS4wVBEEqZtIvGzVds5IYL14ptXQHIpXNpsZokCYJQGuRDa85a0f3qV7+ap556CoDrrruOX/7ylzz33HNEo1HuvPNOfD4fF198cXEHKQiCIJQ8k3UuhaEmSYIgzB3yrTVLtpDyyiuv5Pjx49i2jeM4bNjgdgH7zW9+w8KFCzl48GBmefXCCy/kE5/4BB/72Mfo6upiw4YNfPvb3yYQmFlvXUEQBGH2MVnnUihOkyRBEArLTGvNkhXdDzzwwISv7927N+vnt73tbbztbW8r5JAEQRhBOt+4L5agKuDjui3NNFZI+oMwu5iscykUp0mSIAiFZaa1ZsmKbkEQShfb0Xzxwed5ZH9bpl25ozX/s/MIF61o4rOXbcA0Zm32mjDHmKxzKRSnSZIgCKcX8q0oCMKU+eoje9i27wRak2lXbiiF1rBt3wlu2dZS5BEKQu5M1rnUcWyaqpYR9FXM8MgEQTidENEtCMKU6I4m+cPBDjzjRLI9hsH2/e20h6U7nzB72Lj4UhbUrEKhMl68WmsUigU1q9i4+NIij1AQhNmOpJcIgjAlHjjUj6U13gm2sRyHHz3bykcvlEYSwuzAUAabl1xGNBHmQMfzJO0YPk+QZfWbCPpCxR6eIAinASK6BUGYEpGkk0kpGQ9DKfpjyRka0enLcAHoNQMsb9gkKQ4FJuirYN3CC4o9DEEQTkNEdAuCMCVCXgNH24zdRsTF0ZrKwESxcGEiHO3wwpGHaes7iKPdluRaa45276apapmkOgiCIMxCJKdbEIQpceXSSjzGxJFuj2Fw3ZbmmRnQacgLRx7meM+LaIZs7JRSaDTHe17khSMPF3mEgiAIwlSRSLcgCFOiNujl/OYGto9TTGk5Dpetnj+r/bqL6T+ea0vyJZUbZ2Q8giAIQn4Q0S0IwpS56aIz8Hk9bN/fjuU4GZ9uj2Fw2er5fPayDcUe4rSwHYdbtrUU1X883ZJ8okYtjnY43LMTqCzoWARBEIT8IaJbEIQpYxqKm6/YlIkI98eSVAW8XHdWMw2h0o9wjxfJvmVbC9v2ncBjGGP6jwPcfEVhG6Tk3JLciWGI6BYEQZg1iOgWBGHaNFYEZ5Ut4ESR7LMX1fHUkc6c/McLmWqSc0tyI8DYrVwEQSgU5o3fB8C+9R1FHokwG5FCSkEQ5gzpSPZYnTT/+4XDHOyKTPj3af/xQrK8YROGmvjRbCiDJTXrCjoOQRAEIb+I6BYEYU7QHo7yyP62cSPZWmv6Ykni1vjx45nwH8+1JXlAGrYIwozRMejjPb+Qia5waojoFgRhTvCjZ1uxHT3u66Zp4GhNeyQ27jYz5T8uLckFQRBOPySnWxCEOUFfLDFhJ82mUICugdiEwnym/MelJbkgCMLph4huQRDmBFUBH47W4wpvn2lQ5fcyni4vhv+4tCQXBEE4fZD0EkEQ5gTXbWnGnKST5vL6Ct60cUnG1QTICPXZ7D8uCIIgFB+JdAuCMCdorAhy0YqmjA/3SNKR7NnsPy4IQmFYVhsSm0DhlBHRLQjCnCEdqZ6sk+Zs8x8XBEEQSh8R3YIgzBlMw5BItiAIglAURHQLgjDnkEi2IAiCMNNIIaUgCIIgCIIgFBgR3YIgCIIgCIJQYER0C4IgCIIgCEKBEdEtCIIgCIIgCAVGRLcgCIIgCIIgFBgR3YIgCIIgCIJQYER0C4IgCIIgCEKBEdEtCIIgCIIgCAVGRLcgCIIgCIIgFBgR3YIgCIIgCIJQYER0C4IgCIIgCEKBEdEtCIIgCIIgCAVGRLcgCIIgCIIgFBgR3YIgCIIgCIJQYER0C4IgCIIgCEKBEdEtCIIgCIIgCAVGRLcgCIIgCIIgFBgR3YIgCIIgCIJQYER0C4IgCIIgCEKBEdEtCIIgCIIgCAVGRLcgCIIgCIIgFBgR3YIgCIIgCIJQYEpadLe0tHD55Zfz53/+5xNud/vtt7N27Vo2bNiQ9V9nZ+cMjVQQBEEQBEGYbcyk1vSc6mALxX333cc///M/s3LlSvr7+yfd/pprruGf/umfZmBkgiAIgiAIwmxnprVmyUa64/E499xzD5s2bSr2UARBEARBEITTjJnWmiUruq+99lqamppy3n7v3r289a1v5ayzzuI1r3kNv//97ws4OkEQBEEQBGE2M9Nas2TTS6bCvHnzWLx4MTfeeCONjY3cc889fOADH+C+++5j+fLlOe9Ha83g4CAA0Wg061+heMi5KB3kXJQOci5KBzkXpcNUzoXWGqVUTvsdrg+E4jKV85ZP8qE1ldZaF3icY3Lvvfdy0003jfnal7/8Zd74xjcCbuL6o48+yk9+8pMp7f/aa6/l/PPP52Mf+1hO27e0tJBIJKb0HoIgCIIgzF58Ph8bNmyYcBvRB6VHLucNSk9rFi3Sfc0113DNNdcUbP8LFy6kvb19Sn/j9XpZuXIl4M6SW1tbaW5uJhgMFmKIQo7IuSgd5FyUDnIuSgc5F6XDVM7FSy+9lPN+h+sDobhM5byVmtY8LdJL/u3f/o0tW7Zw7rnnZn63f/9+rr766intRylFWVlZ1u+CweCo3wnFQc5F6SDnonSQc1E6yLkoHXI5F1NJURiI9/CTZ/6B6y8Ql7RiU4zUEsiP1izZQsrJePWrX81TTz0FQG9vL1/4whc4cOAA8Xic733vexw+fJg3vOENRR6lIAiCIAiCMBvJt9Ys2Uj3lVdeyfHjx7FtG8dxMrk7v/nNb1i4cCEHDx7MFDXceOONAFx//fX09vaycuVK/uM//oN58+YVbfyCIAiCIAhC6TLTWrNkRfcDDzww4et79+7N/H+/389nP/tZPvvZzxZ6WIJQUKKJMAc6nidpx/CaAZY3bCLoqyj2sARBEAThtGOmtWbJim5BmEs42uGFIw/T1ncQRzsopdBac7R7N01Vy9i4+FIMNWuzwaZFezjKj55tpS+WoCrg47otzTRWSJGaIAiCMDsR0S0IJcALRx7meM+LGIaZKRJRSqHRHO95EYDNSy4r5hBnDNtxuGVbC4/sb8N2NIZSOFrzPzuPcNGKJj572QZMY25NQARBEITZj3xzCUKRiSbCtPUdxDDMMV83DJO2voNEE+EZHllxuGVbC9v2nUBrMFITEEMptIZt+05wy7aWIo9QEARBEKaOiG5BKDIHOp7H0c6E2zja4UDH8zM0ouLRHo7yyP42PONEsj2Gwfb97bSHpfOfIAiCMLsQ0S0IRSZpxyb1HVVKkbRjMzSi4vGjZ1uxnYmb5FqOw4+ebZ2ZAQmCIAAdgz7e84t1xR6GMMsR0S0IRcZrBtB6YqGptcZrBmZoRMWjL5bIpJSMh6EU/bHkDI1IEAQBltWGsG99R7GHIcxyRHQLQpFZ3rBpUmcSQxksb9g0QyMqHlUBH84kExBHayoD3hkakSAIgiDkBxHdglBkgr4KmqqW4Tj2mK87jk1T1bI54dd93ZZmTGPiSLfHMLhuS/PMDEgQBEEQ8oSIbkEoATYuvpQFNatQqEyqidYahWJBzSo2Lr60yCOcGRorgly0ognLGbuw1HIcLlzRKH7dgiAIwqxDfLoFoQQwlMHmJZdldaT0eYIsq99E0Bcq9vBmlM9e5rbh3b6/HctxMj7dHsPgstXzM68LgiAIwmxCRLcglBBBXwXrFl5Q7GEUFdMwuPmKTZmOlP2xJFUBL9ed1UxDSCLcgiAIwuxERLcgCCVJY0WQj164ttjDEARBEIS8IDndgiAIgiAIglBgRHQLgiAIgiAIQoER0S0IgiAIgiAIBUZEtyAIgiAIgiAUGBHdgiAIgiAIglBgRHQLgiAIgiAIQoER0S0IgiAIgiAIBUZEtyAIgiAIgiAUGBHdgiAIgiAIglBgRHQLgiAIgiAIQoER0S0IgiAIgiAIBUZEtyAIgiAIgiAUGKW11sUeRCnwzDPPoLXG5/MBoLUmmUzi9XpRShV5dHMbORelg5yL0kHORekg56J0mMq5SCQSKKU466yzJtxupD4Qikuu560U8RR7AKXCyJtTKSU3WIkg56J0kHNROsi5KB3kXJQOUzkXSqmcJkkykSotcj1vpYhEugVBEARBEAShwEhOtyAIgiAIgiAUGBHdgiAIgiAIglBgRHQLgiAIgiAIQoER0S0IgiAIgiAIBUZEtyAIgiAIgiAUGBHdgiAIgiAIglBgRHQLgiAIgiAIQoER0S0IgiAIgiAIBUZEtyAIgiAIgiAUGBHdk3DLLbewZs2aYg9jzrJmzRrWr1/Phg0bMv998YtfLPaw5ix33nknF1xwAZs3b+b666/n6NGjxR7SnOPJJ5/Muh82bNjA+vXr5TlVJHbt2sU73/lOzjnnHM4//3w++clP0t3dXexhzUl27NjBO9/5Ts4++2xe+cpX8t3vfveU9nfs2DHe9773sXXrVi655BK+9rWv4ThOnkZbHI4dO8aHP/xhtm7dynnnncdnPvMZ+vv7OXr0KGvWrBn1bDnVYyhk4yn2AEqZ3bt3c++99xZ7GHOe3/zmNyxatKjYw5jz/PCHP+S+++7j7rvvprGxkX/5l3/hP/7jP/jc5z5X7KHNKV72spfR0tKS9btvfvOb7Nmzp0gjmrtYlsX73vc+3vjGN/Kd73yHgYEBbrzxRv7+7/+ef/3Xfy328OYUvb29vPe97+Xaa6/lW9/6FkePHuX9738/CxYs4KqrrprWPj/ykY+wbt06tm3bRldXF+9///upr6/n3e9+d55HP3N84AMfYP369Tz88MOEw2E+/OEP85WvfIUPfvCDAKOeLUJ+kUj3ODiOw+c//3muv/76Yg9FEEqC733ve3z84x9n+fLlhEIhPve5z4ngLgGOHz/OXXfdxU033VTsocw5Ojo66Ojo4JprrsHn81FTU8Pll1/O7t27iz20Ocdzzz3HwMAAH/vYxwgGg6xatYr3vOc9/OxnP5vW/lpaWtizZw+f/OQnqaiooLm5meuvv5577rknzyOfOfr7+1m/fj033ngj5eXlzJs3jze84Q089dRTxR7anEFE9zj8+Mc/xu/387rXva7YQ5nz3HrrrVx88cWcc8453HzzzQwMDBR7SHOOtrY2jh49Sl9fH1dffTVbt27lhhtukGX0EuC2227jTW96EwsWLCj2UOYcTU1NrF27lnvuuYeBgQG6urp48MEHufjii4s9tDmJUirr56qqqmlPgHbu3MnChQupqqrK/G7dunUcPHiQSCRySuMsFpWVlXz5y1+mvr4+87sTJ07Q2NiY+fmmm27iggsu4BWveAW33noryWSyGEM9bRHRPQadnZ3cfvvtfP7zny/2UOY8mzdv5rzzzuPBBx/knnvu4bnnnuMLX/hCsYc15zh58iTgpvrcdddd3HvvvZw8eVIi3UXm6NGjPPjgg7N6uXs2YxgGt99+O7/97W8566yzOO+887AsixtvvLHYQ5tzbNmyhWAwyG233UY0GuXw4cP813/9F319fdPaX29vL5WVlVm/Swvwnp6eUx5vKdDS0sIPfvADPvjBD+Lz+diyZQuXX345v/vd7/j2t7/Nfffdx7/9278Ve5inFSK6x+DLX/4yb3zjG1m5cmWxhzLnueeee7j22mvx+XysWLGCT37yk9x///0kEoliD21OobUG4L3vfS9NTU3MmzePj3zkIzz88MPE4/Eij27u8sMf/pArrriChoaGYg9lTpJIJPjABz7Aq1/9ap566im2b99ORUUFn/zkJ4s9tDlHVVUVd9xxB4899hjnn38+n/rUp7jmmmswTXPa+0w/905Hnn76ad7znvdw4403ct5559HY2MiPf/xjLr/8crxeLxs3buT9738/P//5z4s91NMKEd0jeOyxx3j22Wf58Ic/XOyhCGOwaNEibNumq6ur2EOZU6SXI4dHfhYuXIjWWs5FEXnggQe49NJLiz2MOctjjz3G0aNH+cQnPkFFRQVNTU3ccMMNPPTQQ/T29hZ7eHOOc845h5/+9Kc888wz3HPPPVRXV9PU1DStfdXW1o46h729vSilqK2tzcNoi8fDDz/M+973Pj772c/yzne+c9ztFi5cSGdn52k9+ZhpRHSP4L777qOrq4tLLrmErVu38sY3vhGArVu38qtf/arIo5tb7Nq1i3/6p3/K+t3+/fvx+XxZOWhC4Zk3bx6hUCgrP/LYsWN4vV45F0Vi9+7dHDt2jPPPP7/YQ5mz2LaN4zhZokRW4YpDPB7nF7/4RVa+9R/+8Ae2bNkyrf2tX7+eEydOZNWttLS0sHLlSsrLy095vMXimWee4dOf/jS33XYbr3/96zO/f+yxx7jzzjuztj1w4AALFy4clSsvTB8R3SP4zGc+wwMPPMC9997Lvffey7e//W0A7r33XokozTB1dXXcc889fPvb3yaRSHDw4EFuu+023vKWt5zSkqEwdTweD29+85v55je/yaFDh+jq6uKOO+7gda97HR6POI8Wg127dlFdXU0oFCr2UOYsW7ZsoaysjNtvv51oNEpPTw933nknL3vZy6iuri728OYUXq+Xb3zjG9x5551YlsXvf/977rvvPt71rndNa39nnnkmGzZs4NZbbyUSibB//37uuusurrvuujyPfOawLIvPfe5zfPKTn+SCCy7Ieq2iooI77riDe++9l2QySUtLC9/97ndn9ectRZSWdYMJOXr0KK961avYu3dvsYcyJ3nyySe59dZb2bt3Lz6fjze84Q18/OMfx+/3F3toc45EIsGXv/xlfvWrX5FMJrnyyiu5+eabZ3XUZzbzrW99i//5n//h/vvvL/ZQ5jQ7duzgK1/5Cnv27MHn8/Hyl7+cz3zmM9NOaxCmT0tLC5///OfZv38/8+bN45Of/CSXX375tPd38uRJbr75Zp544glCoRBvfetb+eu//utZG/l96qmnePvb347P5xv12m9+8xt27drFN77xDVpbW6moqOAd73gHf/VXf4VhSHw2X4joFgRBEARBEIQCI9MXQRAEQRAEQSgwIroFQRAEQRAEocCI6BYEQRAEQRCEAiOiWxAEQRAEQRAKjIhuQRAEQRAEQSgwIroFQRAEQRAEocCI6BYEQRAEQRCEAiOiWxCEkmb//v2sWbOGxx9/HIC//Mu/5KabbprRMZx//vncfvvtBX2Pd7zjHXz84x8v6HsIglB6bNiwgZ/+9Kd529/wZ+TPf/5z1qxZQzwez9v+jx07xoYNG/jDH/6Qt33OFaR/syAIU+Id73gHTz31VKb9u9aasrIyzjvvPG644QaWL19e0Pf/3ve+l/O2J0+e5NFHH+Xaa68tyFii0SgXXXQR11133biC+aqrruLMM8/k1ltvLcgYBEEoTUY+K71eL8uWLeOSSy7hXe96FxUVFYDbSTNXfve731FfX8+GDRvG3WYqz8hc+cEPfsDVV19NbW0tCxcunNKYhSEk0i0IwpR59atfTUtLCy0tLezYsYNf/vKXWJbF2972NsLhcLGHl+Ghhx7KawRpJMFgkDe96U387Gc/I5lMjnr9scce48CBA7zzne8s2BgEQShdhj8rf/vb3/KpT32Kxx9/nD/7sz/j6NGjU97f7bffzo4dOwow0vHp7+/nlltuoaenZ0bf93RERLcgCKfMggUL+Nu//Vt6enp45plnALj00ku5/fbbectb3sLWrVsBcByHb37zm1x11VVs2rSJiy++mH/5l3/Btu3MvrZt28bVV1/Npk2bePOb38yePXuy3mtkGsYf//hH3vzmN7N582YuvfRSvvGNb6C15itf+Qq33HILL7zwQtZS6EMPPcS1117LWWedxdatW/nUpz5Fd3d3Zn/79+/n7W9/O1u2bOGyyy7j/vvvn/Czv+1tb6O7u5sHH3xw1Gs/+tGP2LhxI5s2bWL79u1ce+21bNq0iZe//OX81V/9FYcOHRpzn2MtCW/fvp01a9Zkvqjj8Thf+cpXuOyyy9i4cSNXXHEFd99994RjFQSheNTU1PCKV7yC733vezQ0NPB3f/d3AKxZs4Yf/ehHABw9epQPfvCDbN26lS1btvD617+ebdu2AW6a286dO/nSl77EpZdeCoz9nB0rVe2JJ57gda97HZs3b+aaa67JpOul9/H1r389a/s///M/5zOf+Qx79uzhvPPOw7ZtrrnmGm666SaOHj3KmjVr2L59OwCWZXHnnXfy6le/mo0bN456rv/85z/n5S9/OU8//TRveMMb2Lx5M1dddRWPPvpovg9xySOiWxCEvGBZFuAuoab52c9+xsc+9jEee+wxAL7xjW/w4x//mK997Ws8++yzfOMb3+DnP/95Jl/6+PHj3HDDDbzmNa/hySef5Ktf/Sp33XXXuO+5b98+3v/+9/OWt7yFJ554gjvvvJMf/vCHfPe73+XTn/4011xzDRs3bqSlpYXzzz+fxx57jE984hNcf/31PPHEE9x77720t7fz13/914CbKvPhD3+YUCjEI488ws9+9jMefvhh+vv7xx3D4sWLufjii/mv//qvrN+3t7fz29/+lr/4i7+gvb2dD33oQ1xxxRU888wzPPTQQ1iWxac+9anpHWzg7/7u7/jjH//It7/9bZ599lm+8IUv8P/+3/8raGRfEIRTx+v18p73vIc//vGPtLW1Zb3293//91RVVfF///d/PPnkk7z73e/mU5/6FL29vZnAwec+9zkefvjhzN+MfM6Oxd133823vvUtHnvsMc4++2w+8IEPZAUbxuOMM87gu9/9LgD33nsvX/3qV0dt881vfpMf/OAH/NM//RPPPPMMX//61/nhD3/InXfemdlmYGAgM4bHH3+cVatW8elPfxqt9aRjOJ0Q0S0Iwimhtebo0aP84z/+I83NzZx11lmZ184880zOPfdcDMPAcRx++MMf8p73vIf169djGAbr16/nXe96F7/85S8B+N///V/Ky8t5//vfj8/nY/ny/9/evYVEtf0BHP/mDGMjVkqMWZGXTpo9aFJKWVnBgGU9mEpaeGGMDKI0S8lAwR58qEwMH0oq7EpGhqKRVARFSRdIwUtEpZbdHMRrhVPTzPh/EPdx8pL9z5nOOfT7PG1+s1l7zX5Y89u/vdaa+RgMhnGvffXqVXx8fNi8eTMajYaFCxdSXFxMcHDwmOdfvHiRtWvXsnHjRtRqNZ6enmRlZVFXV8fbt29pbm7m1atX7N69m+nTp+Pm5kZ2djZms3nCe5CcnMyTJ0948eKFErty5Qpubm5ERkbi4eFBbW0tKSkpqFQqZsyYwbp162hublYeVn5GX18f1dXV7Nmzh/nz56NSqQgLCyM6Olq5l0KIf68FCxYwODjImzdv7OIfP35ErVaj0WhQq9VERUVRX1+Pm5vbuG2NHGfHs2PHDubMmYNWqyUtLQ2TyURtbe3f8l0uXLhAYmIiwcHBqNVqQkJCiI6OprKyUjnHYrGwc+dOPDw8cHZ2ZsOGDXR3d9Pd3f239OG/QhZSCiF+2o0bN5RXngA6nY7Q0FDOnDnD1KlTlbiXl5dy3NPTQ19fH4cPH7arlgxXOsxmMx0dHXh6eioLjwD8/PzG7Ud7ezvz5s2zi4WGho57fltbG+3t7aMWIalUKt69e6fMRx/Z5qxZsyb8wQMICwvDz8+PsrIy8vLysFqtlJeXEx8fj0ajAeD69etcvnyZd+/eYbFYsNlsWK1WrFar3fedjPb2dmw2G+np6UyZMkWJDw4OotPpfqotIcSvN/ywrVKp7OIZGRlkZWVx584dli9fzurVq4mMjFTGkbGMHGfHExAQoBy7u7szY8YMOjo6/s/e/+njx4/09fXh7+9vF1+wYAHnz5/HZrMpMW9vb+XYxcUFgC9fvvzlPvyXSNIthPhp69evp6io6IfnjZxqMpyMFxQUEBkZOeb5Y21rNdHrx+EK+mRNnTqV+Ph48vLyxvz82rVrY8Ync42EhAQKCgrIysri4cOHdHd3s2XLFmDotWx+fj75+fls2LABrVZLeXk5ubm5k+77yD44OzsDcOnSJYKCgibdhhDi36GpqQknJ6dRuz2tWLGCu3fv8vjxYx48eEBhYSElJSWUl5fj6uo6Zlsjx9nxjHw4h6FxdXgcGcvIdTYTGW8rwrHGzIkq8b8LuQNCiF/C1dUVnU7H06dP7eJdXV0MDAwA4OnpidFotJty8f1CypF8fHxoa2uziz18+JCampoxz/f19R11fZPJRGdnJwCzZ88GsNtV4MOHDxPO6R4WFRWFSqXi5s2bXL16lXXr1uHh4QFAXV0dvr6+xMbGotVqAWhoaBi3reEHFJPJpMRevXqlHHt5eaFWq0d9F6PR+MOpMEKIf5bZbObcuXPo9fpRb9F6enrQaDSEh4eTnZ1NTU0NRqORBw8e/KVrtrS0KMddXV309/cr452zs7NdxdlisUx6Z5WZM2cybdo0nj9/bhd/+fIl3t7ekmh/R+6GEOKXMRgMlJWVce/ePSwWC21tbWzbto1Dhw4BoNfr+fTpE6WlpZjNZlpaWibckSMuLo73799TWlrK169faW1t5cCBA8oPhlarpbOzk97eXkwmEwaDgcbGRkpLSxkYGKC3t5fc3FwMBgM2m42goCB0Oh0nTpzg06dP9PT0cOjQoQkrQsNcXFyIjY2lrKyM2tpakpKSlM+8vb0xGo20t7fz+fNnLl68SGtrKzCU1H9vuPpVVVWF1Wrl2bNnVFdX210rLi6O48eP09DQgNVqpampifj4+AkXngoh/jkWi4X6+noMBgMmk0nZvWTYwMAAERERlJaWYjKZsNlsNDY2Yjab8fHxAYbGtNevX9Pf3/9TixBPnjxJZ2cnAwMDFBcXM336dMLDwwH4448/uH//Pl1dXZhMJoqKiuwq48OFgra2tlFbwjo5OREfH8+FCxdobGzEarXy6NEjKisrlTd94k+SdAshfpmUlBRSUlI4ePAgwcHBJCUlsXLlSnJycoCheYeFhYVUVFQQGhrK/v37SUtLG7c9X19fzp49S1VVFaGhoaSmphIbG8v27duBoeqzxWJhzZo13L59m6CgII4dO0ZVVRXLli1Dr9fz7ds3Tp06hZOTExqNhtOnT9PV1UV4eDibN29Gr9crFaEfSUhIoLm5mYCAALvFnFu3bmXVqlVs2rSJiIgIjEYjJ06cwN/fn5iYmFHV/ICAANLT0zl58iRLly7l6NGjo+5DdnY269evZ9euXSxevJj09HS2bt1KamrqpPoqhHC8GzduEBgYSGBgIEuWLCEnJ4eQkBAqKiqUN2HDXFxcKCkp4datW6xYsYKQkBCOHDnC4cOHlTnTycnJlJWVERERMeZ/A4zFycmJxMREDAYDYWFhNDU1UVJSosyr3rdvH+7u7uj1eiIjI5k7d67d+LVo0SLCwsLYu3cvWVlZo9rPyMggJiaGzMxMQkJCyM/PJzMzc8JF8L+rKYO/234tQgghhBBC/GJS6RZCCCGEEMLBJOkWQgghhBDCwSTpFkIIIYQQwsEk6RZCCCGEEMLBJOkWQgghhBDCwSTpFkIIIYQQwsEk6RZCCCGEEMLBJOkWQgghhBDCwSTpFkIIIYQQwsEk6RZCCCGEEMLBJOkWQgghhBDCwSTpFkIIIYQQwsH+B5EXi/kHMWUMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 800x550 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz = residuals_plot(model,np.stack(train.fp), train[activity_col], np.stack(test.fp), test[activity_col], is_fitted=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f98624bd",
   "metadata": {
    "id": "f98624bd",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
